Basic candle patternsBasic candle patterns marker marks:
- Doji stars
- Doji graves
- Doji dragonflies
- Hammers
- Reversed hammers
- Hanging mans
- Falling stars
- Absorption up/down
- Tweezers up/down
- Three inside ups/downs
ค้นหาในสคริปต์สำหรับ "Up down"
Kawabunga Swing Failure Points Candles (SFP) by RRBKawabunga Swing Failure Points Candles (SFP) by RagingRocketBull 2019
Version 1.0
This indicator shows Swing Failure Points (SFP) and Swing Confirmation Points (SCP) as candles on a chart.
SFP/SCP candles are used by traders as signals for trend confirmation/possible reversal.
The signal is stronger on a higher volume/larger candle size.
A Swing Failure Point (SFP) candle is used to spot a reversal:
- up trend SFP is a failure to close above prev high after making a new higher high => implies reversal down
- down trend SFP is a failure to close below prev low after making a new lower low => implies reversal up
A Swing Confirmation Point (SCP) candle is just the opposite and is used to confirm the current trend:
- up trend SCP is a successful close above prev high after making a new higher high => confirms the trend and implies continuation up
- down trend SCP is a successful close below prev low after making a new lower low => confirms the trend and implies continuation down
Features:
- uses fractal pivots with optional filter
- show/hide SFP/SCP candles, pivots, zigzag, last min/max pivot bands
- dim lag zones/hide false signals introduced by lagging fractals or
- use unconfirmed pivots to eliminate fractal lag/false signals. 2 modes: fractals 1,1 and highest/lowest
- filter only SFP/SCP candles confirmed with volume/candle size
- SFP/SCP candles color highlighting, dim non-important bars
Usage:
- adjust fractal settings to get pivots that best match your data (lower values => more frequent pivots. 0,0 - each candle is a pivot)
- use one of the unconfirmed pivot modes to eliminate false signals or just ignore all signals in the gray lag zones
- optionally filter only SFP/SCP candles with large volume/candle size (volume % change relative to prev bar, abs candle body size value)
- up/down trend SCP (lime/fuchsia) => continuation up/down; up/down trend SFP (orange/aqua) => possible reversal down/up. lime/aqua => up; fuchsia/orange => down.
- when in doubt use show/hide pivots/unconfirmed pivots, min/max pivot bands to see which prev pivot and min/max value were used in comparisons to generate a signal on the following candle.
- disable offset to check on which bar the signal was generated
Notes:
Fractal Pivots:
- SFP/SCP candles depend on fractal pivots, you will get different signals with different pivot settings. Usually 4,4 or 2,2 settings are used to produce fractal pivots, but you can try custom values that fit your data best.
- fractal pivots are a mixed series of highs and lows in no particular order. Pivots must be filtered to produce a proper zigzag where ideally a high is followed by a low and another high in orderly fashion.
Fractal Lag/False Signals:
- only past fractal pivots can be processed on the current bar introducing a lag, therefore, pivots and min/max pivot bands are shown with offset=-rightBars to match their target bars. For unconfirmed pivots an offset=-1 is used with a lag of just 1 bar.
- new pivot is not a confirmed fractal and "does not exist yet" while the distance between it and the current bar is < rightBars => prev old fractal pivot in the same dir is used for comparisons => gives a false signal for that dir
- to show false signals enable lag zones. SFP/SCP candles in lag zones are false. New pivots will be eventually confirmed, but meanwhile you get a false signal because prev pivot in the same dir was used instead.
- to solve this problem you can either temporary hide false signals or completely eliminate them by using unconfirmed pivots of a smaller degree/lag.
- hiding false signals only works for history and should be used only temporary (left disabled). In realtime/replay mode it disables all signals altogether due to TradingView's bug (barcolor doesn't support negative offsets)
Unconfirmed Pivots:
- you have 2 methods to check for unconfirmed pivots: highest/lowest(rightBars) or fractals(1,1) with a min possible step. The first is essentially fractals(0,0) where each candle is a pivot. Both produce more frequent pivots (weaker signals).
- an unconfirmed pivot is used in comparisons to generate a valid signal only when it is a higher high (> max high) or a lower low (< min low) in the dir of a trend. Confirmed pivots of a higher degree are not affected. Zigzag is not affected.
- you can also manually disable the offset to check on which bar the pivot was confirmed. If the pivot just before an SCP/SFP suddenly jumps ahead of it - prev pivot was used, generating a false signal.
- last max high/min low bands can be used to check which value was used in candle comparison to generate a signal: min(pivot min_low, upivot min_low) and max(pivot max_high, upivot max_high) are used
- in the unconfirmed pivots mode the max high/min low pivot bands partially break because you can't have a variable offset to match the random pos of an unconfirmed pivot (anywhere in 0..rightBars from the current bar) to its target bar.
- in the unconfirmed pivots mode h (green) and l (red) pivots become H and L, and h (lime) and l (fuchsia) are used to show unconfirmed pivots of a smaller degree. Some of them will be confirmed later as H and L pivots of a higher degree.
Pivot Filter:
- pivot filter is used to produce a better looking zigzag. Essentially it keeps only higher highs/lower lows in the trend direction until it changes, skipping:
- after a new high: all subsequent lower highs until a new low
- after a new low: all subsequent higher lows until a new high
- you can't filter out all prev highs/lows to keep just the last min/max pivots of the current swing because they were already confirmed as pivots and you can't delete/change history
- alternatively you could just pick the first high following a low and the first low following a high in a sequence and ignore the rest of the pivots in the same dir, producing a crude looking zigzag where obvious max high/min lows are ignored.
- pivot filter affects SCP/SFP signals because it skips some pivots
- pivot filter is not applied to/not affected by the unconfirmed pivots
- zigzag is affected by pivot filter, but not by the unconfirmed pivots. You can't have both high/low on the same bar in a zigzag. High has priority over Low.
- keep same bar pivots option lets you choose which pivots to keep when there are both high/low pivots on the same bar (both kept by default)
SCP/SFP Filters:
- you can confirm/filter only SCP/SFP signals with volume % change/candle size larger than delta. Higher volume/larger candle means stronger signal.
- technically SCP/SFP is always the first matching candle, but it can be invalidated by the following signal in the opposite dir which in turn can be negated by the next signal.
- show first matching SCP/SFP = true - shows only the first signal candle (and any invalidations that follow) and hides further duplicate signals in the same dir, does not highlight the trend.
- show first matching SCP/SFP = false - produces a sequence of candles with duplicate signals, highlights the whole trend until its dir changes (new pivot).
Good Luck! Feel free to learn from/reuse the code to build your own indicators!
Renko CandlesticksRenko charts are awesome . They reduce noise by only painting a brick on the chart when price moves by a specified amount up/down. When the price reverses, it must go twice the specified amount before a brick is painted. Time is not a factor, just price movement. Sometimes however, you want the pros of a renko chart, but on a regular candlestick chart. This indicator attempts to do just that.
A band is placed around price action showing the upper and lower bounds of what would be the current renko brick. The band only goes up/down when the price action itself moves up/down by the amount you specify. There are several ways of specifying the amount:
Fixed Price Amount: As the name says, you enter the brick size amount, i.e. the amount the price has to move before being in a new brick.
% of Price: This method will calculate the amount the price has to move as a percentage of the price itself. This way as price goes up/down, your brick size will adjust accordingly. Recommended values would be around 1% or less.
% of ATR: This option will make the brick size a percentage of the Average True Range. You can specify the ATR time frame to be different from your current time frame as well as the ATR length. For instance you could be on a 10 minute chart but specify the ATR to be daily with a length of 3 and a percentage amount of 15. This would make your brick size 15% of the Average True Range for the last 3 days. Recommended values are 10 to 20%.
Use this indicator on any time frame, even the 1 minute as the renko bands span the price action the same way on any time frame easily letting you know whether or not the price has moved appreciably, regardless of how much time has passed.
You can also set alerts easily, simply set the alert to crossing and choose “Renko Candlesticks” instead of “Value”. You will then see the options for the renko upper and lower bounds.
Tested on Bitcoin with the following values:
Fixed Price Amount: 30 ($30)
% of Price: 0.45 (if Bitcoin is $7000 then the brick size would be $31.50)
% of ATR: 15%, ATR Time Frame: 1D, ATR Length: 3 (3 days)
Impulses-1Lines "Total Up Impulses" and "Total Down Impulses" are the sum of impulses in the last n periods (Length).
line 1 => "Total Up Impulses": the sum of up impulses.
line 2 => "Total Down Impulses": the sum of down impulses.
When line 1 crosses up line 2, it indicates an uptrend is comming out.
When line 1 crosses down line 2, it indicates a downtrend is comming out.
Fibonacci Commodity Stenth IndexFibonacci Commodity Strength Value tells us about the strength and weakness of bull or bear market.
The main focus in this is too be done at reversal. It can also be used for identifying fake ups/downs.
If all the 4 lines moves upward after a huge up spike, then notice the values of all 4 values. If red fib is smaller than green fib then it is a fake trend. If its more then its uptrend and same for bear movement. ;)
It also represents cci (in terms of values) and rsi (in terms of waves).
Enjoy !!!!!
PineStats█ OVERVIEW
PineStats is a comprehensive statistical analysis library for Pine Script v6, providing 104 functions across 6 modules. Built for quantitative traders, researchers, and indicator developers who need professional-grade statistics without reinventing the wheel.
For building mean-reversion strategies, analyzing return distributions, measuring correlations, or testing for market regimes.
█ MODULES
CORE STATISTICS (20 functions)
• Central tendency: mean, median, WMA, EMA
• Dispersion: variance, stdev, MAD, range
• Standardization: z-score, robust z-score, normalize, percentile
• Distribution shape: skewness, kurtosis
PROBABILITY DISTRIBUTIONS (17 functions)
• Normal: PDF, CDF, inverse CDF (quantile function)
• Power-law: Hill estimator, MLE alpha, survival function
• Exponential: PDF, CDF, rate estimation
• Normality testing: Jarque-Bera test
ENTROPY (9 functions)
• Shannon entropy (information theory)
• Tsallis entropy (non-extensive, fat-tail sensitive)
• Permutation entropy (ordinal patterns)
• Approximate entropy (regularity measure)
• Entropy-based regime detection
PROBABILITY (21 functions)
• Win rates and expected value
• First passage time estimation
• TP/SL probability analysis
• Conditional probability and Bayes updates
• Streak and drawdown probabilities
REGRESSION (19 functions)
• Linear regression: slope, intercept, forecast
• Goodness of fit: R², adjusted R², standard error
• Statistical tests: t-statistic, p-value, significance
• Trend analysis: strength, angle, acceleration
• Quadratic regression
CORRELATION (18 functions)
• Pearson, Spearman, Kendall correlation
• Covariance, beta, alpha (Jensen's)
• Rolling correlation analysis
• Autocorrelation and cross-correlation
• Information ratio, tracking error
█ QUICK START
import HenriqueCentieiro/PineStats/1 as stats
// Z-score for mean reversion
z = stats.zscore(close, 20)
// Test if returns are normally distributed
returns = (close - close ) / close
isGaussian = stats.is_normal(returns, 100, 0.05)
// Regression channel
= stats.linreg_channel(close, 50, 2.0)
// Correlation with benchmark
spyReturns = request.security("SPY", timeframe.period, close/close - 1)
beta = stats.beta(returns, spyReturns, 60)
█ USE CASES
✓ Mean Reversion — z-scores, percentiles, Bollinger-style analysis
✓ Regime Detection — entropy measures, correlation regimes
✓ Risk Analysis — drawdown probability, VaR via quantiles
✓ Strategy Evaluation — expected value, win rates, R:R analysis
✓ Distribution Analysis — normality tests, fat-tail detection
✓ Multi-Asset — beta, alpha, correlation, relative strength
█ NOTES
• All functions return `na` on invalid inputs
• Designed for Pine Script v6
• Fully documented in the library header
• Part of the Pine ecosystem: PineStats, PineQuant, PineCriticality, PineWavelet
█ REFERENCES
• Abramowitz & Stegun — Normal CDF approximation
• Acklam's algorithm — Inverse normal CDF
• Hill estimator — Power-law tail estimation
• Tsallis statistics — Non-extensive entropy
Full documentation in the library header.
mean(src, length)
Calculates the arithmetic mean (simple moving average) over a lookback period
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: Arithmetic mean of the last `length` values, or `na` if inputs invalid
wma_custom(src, length)
Calculates weighted moving average with linearly decreasing weights
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: Weighted moving average, or `na` if inputs invalid
ema_custom(src, length)
Calculates exponential moving average
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: Exponential moving average, or `na` if inputs invalid
median(src, length)
Calculates the median value over a lookback period
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: Median value, or `na` if inputs invalid
variance(src, length)
Calculates population variance over a lookback period
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: Population variance, or `na` if inputs invalid
stdev(src, length)
Calculates population standard deviation over a lookback period
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: Population standard deviation, or `na` if inputs invalid
mad(src, length)
Calculates Median Absolute Deviation (MAD) - robust dispersion measure
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: MAD value, or `na` if inputs invalid
data_range(src, length)
Calculates the range (highest - lowest) over a lookback period
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: Range value, or `na` if inputs invalid
zscore(src, length)
Calculates z-score (number of standard deviations from mean)
Parameters:
src (float) : Source series
length (simple int) : Lookback period for mean and stdev calculation (must be >= 2)
Returns: Z-score, or `na` if inputs invalid or stdev is zero
zscore_robust(src, length)
Calculates robust z-score using median and MAD (resistant to outliers)
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 2)
Returns: Robust z-score, or `na` if inputs invalid or MAD is zero
normalize(src, length)
Normalizes value to range using min-max scaling
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: Normalized value in , or `na` if inputs invalid or range is zero
percentile(src, length)
Calculates percentile rank of current value within lookback window
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: Percentile rank (0 to 100), or `na` if inputs invalid
winsorize(src, length, lower_pct, upper_pct)
Winsorizes values by clamping to percentile bounds (reduces outlier impact)
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
lower_pct (simple float) : Lower percentile bound (0-100, e.g., 5 for 5th percentile)
upper_pct (simple float) : Upper percentile bound (0-100, e.g., 95 for 95th percentile)
Returns: Winsorized value clamped to bounds
skewness(src, length)
Calculates sample skewness (measure of distribution asymmetry)
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 3)
Returns: Skewness value (negative = left tail, positive = right tail), or `na` if invalid
kurtosis(src, length)
Calculates excess kurtosis (measure of distribution tail heaviness)
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 4)
Returns: Excess kurtosis (>0 = heavy tails, <0 = light tails), or `na` if invalid
count_valid(src, length)
Counts non-na values in lookback window (useful for data quality checks)
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: Count of valid (non-na) values
sum(src, length)
Calculates sum over lookback period
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: Sum of values, or `na` if inputs invalid
cumsum(src)
Calculates cumulative sum (running total from first bar)
Parameters:
src (float) : Source series
Returns: Cumulative sum
change(src, length)
Returns the change (difference) from n bars ago
Parameters:
src (float) : Source series
length (simple int) : Number of bars to look back (must be >= 1)
Returns: Current value minus value from `length` bars ago
roc(src, length)
Calculates Rate of Change (percentage change from n bars ago)
Parameters:
src (float) : Source series
length (simple int) : Number of bars to look back (must be >= 1)
Returns: Percentage change as decimal (0.05 = 5%), or `na` if invalid
normal_pdf_standard(x)
Calculates the standard normal probability density function (PDF)
Parameters:
x (float) : The value to evaluate
Returns: PDF value at x for standard normal N(0,1)
normal_pdf(x, mu, sigma)
Calculates the normal probability density function (PDF)
Parameters:
x (float) : The value to evaluate
mu (float) : Mean of the distribution (default: 0)
sigma (float) : Standard deviation (default: 1, must be > 0)
Returns: PDF value at x for normal N(mu, sigma²)
normal_cdf_standard(x)
Calculates the standard normal cumulative distribution function (CDF)
Parameters:
x (float) : The value to evaluate
Returns: Probability P(X <= x) for standard normal N(0,1)
@description Uses Abramowitz & Stegun approximation (formula 7.1.26), accurate to ~1.5e-7
normal_cdf(x, mu, sigma)
Calculates the normal cumulative distribution function (CDF)
Parameters:
x (float) : The value to evaluate
mu (float) : Mean of the distribution (default: 0)
sigma (float) : Standard deviation (default: 1, must be > 0)
Returns: Probability P(X <= x) for normal N(mu, sigma²)
normal_inv_standard(p)
Calculates the inverse standard normal CDF (quantile function)
Parameters:
p (float) : Probability value (must be in (0, 1))
Returns: x such that P(X <= x) = p for standard normal N(0,1)
@description Uses Acklam's algorithm, accurate to ~1.15e-9
normal_inv(p, mu, sigma)
Calculates the inverse normal CDF (quantile function)
Parameters:
p (float) : Probability value (must be in (0, 1))
mu (float) : Mean of the distribution
sigma (float) : Standard deviation (must be > 0)
Returns: x such that P(X <= x) = p for normal N(mu, sigma²)
power_law_alpha(src, length, tail_pct)
Estimates power-law exponent (alpha) using Hill estimator
Parameters:
src (float) : Source series (typically absolute returns or drawdowns)
length (simple int) : Lookback period (must be >= 10 for reliable estimates)
tail_pct (simple float) : Percentage of data to use for tail estimation (default: 0.1 = top 10%)
Returns: Estimated alpha (tail index), typically 2-4 for financial data
@description Alpha < 2 indicates infinite variance (very heavy tails)
@description Alpha < 3 indicates infinite kurtosis
@description Alpha > 4 suggests near-Gaussian behavior
power_law_alpha_mle(src, length, x_min)
Estimates power-law alpha using maximum likelihood (Clauset method)
Parameters:
src (float) : Source series (positive values expected)
length (simple int) : Lookback period (must be >= 20)
x_min (float) : Minimum threshold for power-law behavior
Returns: Estimated alpha using MLE
power_law_pdf(x, alpha, x_min)
Calculates power-law probability density (Pareto Type I)
Parameters:
x (float) : Value to evaluate (must be >= x_min)
alpha (float) : Power-law exponent (must be > 1)
x_min (float) : Minimum value / scale parameter (must be > 0)
Returns: PDF value
power_law_survival(x, alpha, x_min)
Calculates power-law survival function P(X > x)
Parameters:
x (float) : Value to evaluate (must be >= x_min)
alpha (float) : Power-law exponent (must be > 1)
x_min (float) : Minimum value / scale parameter (must be > 0)
Returns: Probability of exceeding x
power_law_ks(src, length, alpha, x_min)
Tests if data follows power-law using simplified Kolmogorov-Smirnov
Parameters:
src (float) : Source series
length (simple int) : Lookback period
alpha (float) : Estimated alpha from power_law_alpha()
x_min (float) : Threshold value
Returns: KS statistic (lower = better fit, typically < 0.1 for good fit)
is_power_law(src, length, tail_pct, ks_threshold)
Simple test if distribution appears to follow power-law
Parameters:
src (float) : Source series
length (simple int) : Lookback period
tail_pct (simple float) : Tail percentage for alpha estimation
ks_threshold (simple float) : Maximum KS statistic for acceptance (default: 0.1)
Returns: true if KS test suggests power-law fit
exp_pdf(x, lambda)
Calculates exponential probability density function
Parameters:
x (float) : Value to evaluate (must be >= 0)
lambda (float) : Rate parameter (must be > 0)
Returns: PDF value
exp_cdf(x, lambda)
Calculates exponential cumulative distribution function
Parameters:
x (float) : Value to evaluate (must be >= 0)
lambda (float) : Rate parameter (must be > 0)
Returns: Probability P(X <= x)
exp_lambda(src, length)
Estimates exponential rate parameter (lambda) using MLE
Parameters:
src (float) : Source series (positive values)
length (simple int) : Lookback period
Returns: Estimated lambda (1/mean)
jarque_bera(src, length)
Calculates Jarque-Bera test statistic for normality
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 10)
Returns: JB statistic (higher = more deviation from normality)
@description Under normality, JB ~ chi-squared(2). JB > 6 suggests non-normality at 5% level
is_normal(src, length, significance)
Tests if distribution is approximately normal
Parameters:
src (float) : Source series
length (simple int) : Lookback period
significance (simple float) : Significance level (default: 0.05)
Returns: true if Jarque-Bera test does not reject normality
shannon_entropy(src, length, n_bins)
Calculates Shannon entropy from a probability distribution
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 10)
n_bins (simple int) : Number of histogram bins for discretization (default: 10)
Returns: Shannon entropy in bits (log base 2)
@description Higher entropy = more randomness/uncertainty, lower = more predictability
shannon_entropy_norm(src, length, n_bins)
Calculates normalized Shannon entropy
Parameters:
src (float) : Source series
length (simple int) : Lookback period
n_bins (simple int) : Number of histogram bins
Returns: Normalized entropy where 0 = perfectly predictable, 1 = maximum randomness
tsallis_entropy(src, length, q, n_bins)
Calculates Tsallis entropy with q-parameter
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 10)
q (float) : Entropic index (q=1 recovers Shannon entropy)
n_bins (simple int) : Number of histogram bins
Returns: Tsallis entropy value
@description q < 1: emphasizes rare events (fat tails)
@description q = 1: equivalent to Shannon entropy
@description q > 1: emphasizes common events
optimal_q(src, length)
Estimates optimal q parameter from kurtosis
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Estimated q value that best captures the distribution's tail behavior
@description Uses relationship: q ≈ (5 + kurtosis) / (3 + kurtosis) for kurtosis > 0
tsallis_q_gaussian(x, q, beta)
Calculates Tsallis q-Gaussian probability density
Parameters:
x (float) : Value to evaluate
q (float) : Tsallis q parameter (must be < 3)
beta (float) : Width parameter (inverse temperature, must be > 0)
Returns: q-Gaussian PDF value
@description q=1 recovers standard Gaussian
permutation_entropy(src, length, order)
Calculates permutation entropy (ordinal pattern complexity)
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 20)
order (simple int) : Embedding dimension / pattern length (2-5, default: 3)
Returns: Normalized permutation entropy
@description Measures complexity of temporal ordering patterns
@description 0 = perfectly predictable sequence, 1 = random
approx_entropy(src, length, m, r)
Calculates Approximate Entropy (ApEn) - regularity measure
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 50)
m (simple int) : Embedding dimension (default: 2)
r (simple float) : Tolerance as fraction of stdev (default: 0.2)
Returns: Approximate entropy value (higher = more irregular/complex)
@description Lower ApEn indicates more self-similarity and predictability
entropy_regime(src, length, q, n_bins)
Detects market regime based on entropy level
Parameters:
src (float) : Source series (typically returns)
length (simple int) : Lookback period
q (float) : Tsallis q parameter (use optimal_q() or default 1.5)
n_bins (simple int) : Number of histogram bins
Returns: Regime indicator: -1 = trending (low entropy), 0 = transition, 1 = ranging (high entropy)
entropy_risk(src, length)
Calculates entropy-based risk indicator
Parameters:
src (float) : Source series (typically returns)
length (simple int) : Lookback period
Returns: Risk score where 1 = maximum divergence from Gaussian 1
hit_rate(src, length)
Calculates hit rate (probability of positive outcome) over lookback
Parameters:
src (float) : Source series (positive values count as hits)
length (simple int) : Lookback period
Returns: Hit rate as decimal
hit_rate_cond(condition, length)
Calculates hit rate for custom condition over lookback
Parameters:
condition (bool) : Boolean series (true = hit)
length (simple int) : Lookback period
Returns: Hit rate as decimal
expected_value(src, length)
Calculates expected value of a series
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Expected value (mean)
expected_value_trade(win_prob, take_profit, stop_loss)
Calculates expected value for a trade with TP and SL levels
Parameters:
win_prob (float) : Probability of hitting TP (0-1)
take_profit (float) : Take profit in price units or %
stop_loss (float) : Stop loss in price units or % (positive value)
Returns: Expected value per trade
@description EV = (win_prob * TP) - ((1 - win_prob) * SL)
breakeven_winrate(take_profit, stop_loss)
Calculates breakeven win rate for given TP/SL ratio
Parameters:
take_profit (float) : Take profit distance
stop_loss (float) : Stop loss distance
Returns: Required win rate for breakeven (EV = 0)
reward_risk_ratio(take_profit, stop_loss)
Calculates the reward-to-risk ratio
Parameters:
take_profit (float) : Take profit distance
stop_loss (float) : Stop loss distance
Returns: R:R ratio
fpt_probability(src, length, target, max_bars)
Estimates probability of price reaching target within N bars
Parameters:
src (float) : Source series (typically returns)
length (simple int) : Lookback for volatility estimation
target (float) : Target move (in same units as src, e.g., % return)
max_bars (simple int) : Maximum bars to consider
Returns: Probability of reaching target within max_bars
@description Based on random walk with drift approximation
fpt_mean(src, length, target)
Estimates mean first passage time to target level
Parameters:
src (float) : Source series (typically returns)
length (simple int) : Lookback for volatility estimation
target (float) : Target move
Returns: Expected number of bars to reach target (can be infinite)
fpt_historical(src, length, target)
Counts historical bars to reach target from each point
Parameters:
src (float) : Source series (typically price or returns)
length (simple int) : Lookback period
target (float) : Target move from each starting point
Returns: Array of first passage times (na if target not reached within lookback)
tp_probability(src, length, tp_distance, sl_distance)
Estimates probability of hitting TP before SL
Parameters:
src (float) : Source series (typically returns)
length (simple int) : Lookback for estimation
tp_distance (float) : Take profit distance (positive)
sl_distance (float) : Stop loss distance (positive)
Returns: Probability of TP being hit first
trade_probability(src, length, tp_pct, sl_pct)
Calculates complete trade probability and EV analysis
Parameters:
src (float) : Source series (typically returns)
length (simple int) : Lookback period
tp_pct (float) : Take profit percentage
sl_pct (float) : Stop loss percentage
Returns: Tuple:
cond_prob(condition_a, condition_b, length)
Calculates conditional probability P(B|A) from historical data
Parameters:
condition_a (bool) : Condition A (the given condition)
condition_b (bool) : Condition B (the outcome)
length (simple int) : Lookback period
Returns: P(B|A) = P(A and B) / P(A)
bayes_update(prior, likelihood, false_positive)
Updates probability using Bayes' theorem
Parameters:
prior (float) : Prior probability P(H)
likelihood (float) : P(E|H) - probability of evidence given hypothesis
false_positive (float) : P(E|~H) - probability of evidence given hypothesis is false
Returns: Posterior probability P(H|E)
streak_prob(win_rate, streak_length)
Calculates probability of N consecutive wins given win rate
Parameters:
win_rate (float) : Single-trade win probability
streak_length (simple int) : Number of consecutive wins
Returns: Probability of streak
losing_streak_prob(win_rate, streak_length)
Calculates probability of experiencing N consecutive losses
Parameters:
win_rate (float) : Single-trade win probability
streak_length (simple int) : Number of consecutive losses
Returns: Probability of losing streak
drawdown_prob(src, length, dd_threshold)
Estimates probability of drawdown exceeding threshold
Parameters:
src (float) : Source series (returns)
length (simple int) : Lookback period
dd_threshold (float) : Drawdown threshold (as positive decimal, e.g., 0.10 = 10%)
Returns: Historical probability of exceeding drawdown threshold
prob_to_odds(prob)
Calculates odds from probability
Parameters:
prob (float) : Probability (0-1)
Returns: Odds (prob / (1 - prob))
odds_to_prob(odds)
Calculates probability from odds
Parameters:
odds (float) : Odds ratio
Returns: Probability (0-1)
implied_prob(decimal_odds)
Calculates implied probability from decimal odds (betting)
Parameters:
decimal_odds (float) : Decimal odds (e.g., 2.5 means $2.50 return per $1 bet)
Returns: Implied probability
logit(prob)
Calculates log-odds (logit) from probability
Parameters:
prob (float) : Probability (must be in (0, 1))
Returns: Log-odds
inv_logit(log_odds)
Calculates probability from log-odds (inverse logit / sigmoid)
Parameters:
log_odds (float) : Log-odds value
Returns: Probability (0-1)
linreg_slope(src, length)
Calculates linear regression slope
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 2)
Returns: Slope coefficient (change per bar)
linreg_intercept(src, length)
Calculates linear regression intercept
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 2)
Returns: Intercept (predicted value at oldest bar in window)
linreg_value(src, length)
Calculates predicted value at current bar using linear regression
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Predicted value at current bar (end of regression line)
linreg_forecast(src, length, offset)
Forecasts value N bars ahead using linear regression
Parameters:
src (float) : Source series
length (simple int) : Lookback period for regression
offset (simple int) : Bars ahead to forecast (positive = future)
Returns: Forecasted value
linreg_channel(src, length, mult)
Calculates linear regression channel with bands
Parameters:
src (float) : Source series
length (simple int) : Lookback period
mult (simple float) : Standard deviation multiplier for bands
Returns: Tuple:
r_squared(src, length)
Calculates R-squared (coefficient of determination)
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: R² value where 1 = perfect linear fit
adj_r_squared(src, length)
Calculates adjusted R-squared (accounts for sample size)
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Adjusted R² value
std_error(src, length)
Calculates standard error of estimate (residual standard deviation)
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Standard error
residual(src, length)
Calculates residual at current bar
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Residual (actual - predicted)
residuals(src, length)
Returns array of all residuals in lookback window
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Array of residuals
t_statistic(src, length)
Calculates t-statistic for slope coefficient
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: T-statistic (slope / standard error of slope)
slope_pvalue(src, length)
Approximates p-value for slope t-test (two-tailed)
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Approximate p-value
is_significant(src, length, alpha)
Tests if regression slope is statistically significant
Parameters:
src (float) : Source series
length (simple int) : Lookback period
alpha (simple float) : Significance level (default: 0.05)
Returns: true if slope is significant at alpha level
trend_strength(src, length)
Calculates normalized trend strength based on R² and slope
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Trend strength where sign indicates direction
trend_angle(src, length)
Calculates trend angle in degrees
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Angle in degrees (positive = uptrend, negative = downtrend)
linreg_acceleration(src, length)
Calculates trend acceleration (second derivative)
Parameters:
src (float) : Source series
length (simple int) : Lookback period for each regression
Returns: Acceleration (change in slope)
linreg_deviation(src, length)
Calculates deviation from regression line in standard error units
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Deviation in standard error units (like z-score)
quadreg_coefficients(src, length)
Fits quadratic regression and returns coefficients
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 4)
Returns: Tuple: for y = a*x² + b*x + c
quadreg_value(src, length)
Calculates quadratic regression value at current bar
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Predicted value from quadratic fit
correlation(x, y, length)
Calculates Pearson correlation coefficient between two series
Parameters:
x (float) : First series
y (float) : Second series
length (simple int) : Lookback period (must be >= 3)
Returns: Correlation coefficient
covariance(x, y, length)
Calculates sample covariance between two series
Parameters:
x (float) : First series
y (float) : Second series
length (simple int) : Lookback period (must be >= 2)
Returns: Covariance value
beta(asset, benchmark, length)
Calculates beta coefficient (slope of regression of y on x)
Parameters:
asset (float) : Asset returns series
benchmark (float) : Benchmark returns series
length (simple int) : Lookback period
Returns: Beta coefficient
@description Beta = Cov(asset, benchmark) / Var(benchmark)
alpha(asset, benchmark, length, risk_free)
Calculates alpha (Jensen's alpha / intercept)
Parameters:
asset (float) : Asset returns series
benchmark (float) : Benchmark returns series
length (simple int) : Lookback period
risk_free (float) : Risk-free rate (default: 0)
Returns: Alpha value (excess return not explained by beta)
spearman(x, y, length)
Calculates Spearman rank correlation coefficient
Parameters:
x (float) : First series
y (float) : Second series
length (simple int) : Lookback period (must be >= 3)
Returns: Spearman correlation
@description More robust to outliers than Pearson correlation
kendall_tau(x, y, length)
Calculates Kendall's tau rank correlation (simplified)
Parameters:
x (float) : First series
y (float) : Second series
length (simple int) : Lookback period (must be >= 3)
Returns: Kendall's tau
correlation_change(x, y, length, change_period)
Calculates change in correlation over time
Parameters:
x (float) : First series
y (float) : Second series
length (simple int) : Lookback period for correlation
change_period (simple int) : Period over which to measure change
Returns: Change in correlation
correlation_regime(x, y, length, ma_length)
Detects correlation regime based on level and stability
Parameters:
x (float) : First series
y (float) : Second series
length (simple int) : Lookback period for correlation
ma_length (simple int) : Moving average length for smoothing
Returns: Regime: -1 = negative, 0 = uncorrelated, 1 = positive
correlation_stability(x, y, length, stability_length)
Calculates correlation stability (inverse of volatility)
Parameters:
x (float) : First series
y (float) : Second series
length (simple int) : Lookback for correlation
stability_length (simple int) : Lookback for stability calculation
Returns: Stability score where 1 = perfectly stable
relative_strength(asset, benchmark, length)
Calculates relative strength of asset vs benchmark
Parameters:
asset (float) : Asset price series
benchmark (float) : Benchmark price series
length (simple int) : Smoothing period
Returns: Relative strength ratio (normalized)
tracking_error(asset, benchmark, length)
Calculates tracking error (standard deviation of excess returns)
Parameters:
asset (float) : Asset returns
benchmark (float) : Benchmark returns
length (simple int) : Lookback period
Returns: Tracking error (annualize by multiplying by sqrt(252) for daily data)
information_ratio(asset, benchmark, length)
Calculates information ratio (risk-adjusted excess return)
Parameters:
asset (float) : Asset returns
benchmark (float) : Benchmark returns
length (simple int) : Lookback period
Returns: Information ratio
capture_ratio(asset, benchmark, length, up_capture)
Calculates up/down capture ratio
Parameters:
asset (float) : Asset returns
benchmark (float) : Benchmark returns
length (simple int) : Lookback period
up_capture (simple bool) : If true, calculate up capture; if false, down capture
Returns: Capture ratio
autocorrelation(src, length, lag)
Calculates autocorrelation at specified lag
Parameters:
src (float) : Source series
length (simple int) : Lookback period
lag (simple int) : Lag for autocorrelation (default: 1)
Returns: Autocorrelation at specified lag
partial_autocorr(src, length)
Calculates partial autocorrelation at lag 1
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: PACF at lag 1 (equals ACF at lag 1)
autocorr_test(src, length, max_lag)
Tests for significant autocorrelation (Ljung-Box inspired)
Parameters:
src (float) : Source series
length (simple int) : Lookback period
max_lag (simple int) : Maximum lag to test
Returns: Sum of squared autocorrelations (higher = more autocorrelation)
cross_correlation(x, y, length, lag)
Calculates cross-correlation at specified lag
Parameters:
x (float) : First series
y (float) : Second series (lagged)
length (simple int) : Lookback period
lag (simple int) : Lag to apply to y (positive = y leads x)
Returns: Cross-correlation at specified lag
cross_correlation_peak(x, y, length, max_lag)
Finds lag with maximum cross-correlation
Parameters:
x (float) : First series
y (float) : Second series
length (simple int) : Lookback period
max_lag (simple int) : Maximum lag to search (both directions)
Returns: Tuple:
EL OJO DE DIOS - FINAL (ORDEN CORREGIDO)//@version=6
indicator("EL OJO DE DIOS - FINAL (ORDEN CORREGIDO)", overlay=true, max_boxes_count=500, max_lines_count=500, max_labels_count=500)
// --- 1. CONFIGURACIÓN ---
grpEMA = "Medias Móviles"
inpShowEMA = input.bool(true, "Mostrar EMAs", group=grpEMA)
inpEMA21 = input.int(21, "EMA 21", minval=1, group=grpEMA)
inpEMA50 = input.int(50, "EMA 50", minval=1, group=grpEMA)
inpEMA200 = input.int(200, "EMA 200", minval=1, group=grpEMA)
grpStrategy = "Estrategia"
inpTrendTF = input.string("Current", "Timeframe Señal", options= , group=grpStrategy)
inpADXFilter = input.bool(true, "Filtro ADX", group=grpStrategy)
inpADXPeriod = input.int(14, "Período ADX", group=grpStrategy)
inpADXLimit = input.int(20, "Límite ADX", group=grpStrategy)
inpRR = input.float(2.0, "Riesgo:Beneficio", group=grpStrategy)
grpVisuals = "Visuales"
inpShowPrevDay = input.bool(true, "Máx/Mín Ayer", group=grpVisuals)
inpShowNY = input.bool(true, "Sesión NY", group=grpVisuals)
// --- 2. VARIABLES ---
var float t1Price = na
var bool t1Bull = false
var bool t1Conf = false
var line slLine = na
var line tpLine = na
// Variables Prev Day
var float pdH = na
var float pdL = na
var line linePDH = na
var line linePDL = na
// Variables Session
var box nySessionBox = na
// --- 3. CÁLCULO ADX MANUAL ---
f_calcADX(_high, _low, _close, _len) =>
// True Range Manual
tr = math.max(_high - _low, math.abs(_high - _close ), math.abs(_low - _close ))
// Directional Movement
up = _high - _high
down = _low - _low
plusDM = (up > down and up > 0) ? up : 0.0
minusDM = (down > up and down > 0) ? down : 0.0
// Smoothed averages
atr = ta.rma(tr, _len)
plus = 100.0 * ta.rma(plusDM, _len) / atr
minus = 100.0 * ta.rma(minusDM, _len) / atr
// DX y ADX
sum = plus + minus
dx = sum == 0 ? 0.0 : 100.0 * math.abs(plus - minus) / sum
adx = ta.rma(dx, _len)
adx
// --- 4. CÁLCULO DE DATOS ---
ema21 = ta.ema(close, inpEMA21)
ema50 = ta.ema(close, inpEMA50)
ema200 = ta.ema(close, inpEMA200)
// MTF Logic
targetTF = inpTrendTF == "Current" ? timeframe.period : inpTrendTF == "15m" ? "15" : "60"
// CORRECCIÓN AQUÍ: Uso de argumentos nominales (gaps=, lookahead=) para evitar errores de orden
f_getSeries(src, tf) =>
tf == timeframe.period ? src : request.security(syminfo.tickerid, tf, src, gaps=barmerge.gaps_on, lookahead=barmerge.lookahead_off)
tf_close = f_getSeries(close, targetTF)
tf_high = f_getSeries(high, targetTF)
tf_low = f_getSeries(low, targetTF)
tf_ema21 = ta.ema(tf_close, inpEMA21)
tf_ema50 = ta.ema(tf_close, inpEMA50)
// Calcular ADX
float tf_adx = f_calcADX(tf_high, tf_low, tf_close, inpADXPeriod)
// Cruces
bool crossUp = ta.crossover(tf_ema21, tf_ema50)
bool crossDown = ta.crossunder(tf_ema21, tf_ema50)
bool crossSignal = crossUp or crossDown
bool adxOk = inpADXFilter ? tf_adx > inpADXLimit : true
// --- 5. LÓGICA DE SEÑALES ---
if crossSignal and adxOk and barstate.isconfirmed
t1Price := tf_ema21
t1Bull := tf_ema21 > tf_ema50
t1Conf := false
if not na(slLine)
line.delete(slLine)
slLine := na
if not na(tpLine)
line.delete(tpLine)
tpLine := na
label.new(bar_index, high + (ta.atr(14)*0.5), text="CRUCE T1", color=t1Bull ? color.green : color.red, textcolor=color.white, size=size.small)
bool touch = false
if not na(t1Price) and not t1Conf
if t1Bull
touch := low <= t1Price and close >= t1Price
else
touch := high >= t1Price and close <= t1Price
if touch and barstate.isconfirmed
t1Conf := true
float atr = ta.atr(14)
float sl = t1Bull ? low - (atr*0.1) : high + (atr*0.1)
float dist = math.abs(t1Price - sl)
float tp = t1Bull ? t1Price + (dist * inpRR) : t1Price - (dist * inpRR)
label.new(bar_index, t1Price, text="ENTRADA", color=color.yellow, textcolor=color.black, size=size.small)
slLine := line.new(bar_index, sl, bar_index + 15, sl, color=color.red, style=line.style_dashed, width=2)
tpLine := line.new(bar_index, tp, bar_index + 15, tp, color=color.green, style=line.style_dashed, width=2)
// --- 6. GRÁFICO ---
col21 = ema21 > ema21 ? color.teal : color.maroon
col50 = ema50 > ema50 ? color.aqua : color.fuchsia
col200 = ema200 > ema200 ? color.blue : color.red
plot(inpShowEMA ? ema21 : na, "EMA21", color=col21, linewidth=2)
plot(inpShowEMA ? ema50 : na, "EMA50", color=col50, linewidth=2)
plot(inpShowEMA ? ema200 : na, "EMA200", color=col200, linewidth=2)
bgcolor(ema50 > ema200 ? color.new(color.green, 95) : color.new(color.red, 95))
// --- 7. SESIÓN NY ---
isNYSummer = (month(time) == 3 and dayofmonth(time) >= 14) or (month(time) > 3 and month(time) < 11)
hourOffset = isNYSummer ? 4 : 5
nyHour = (hour - hourOffset) % 24
bool isSession = nyHour >= 6 and nyHour < 11
if isSession and inpShowNY
if na(nySessionBox)
nySessionBox := box.new(bar_index, high, bar_index, low, bgcolor=color.new(color.blue, 92), border_color=color.new(color.white, 0))
else
box.set_right(nySessionBox, bar_index)
box.set_top(nySessionBox, math.max(high, box.get_top(nySessionBox)))
box.set_bottom(nySessionBox, math.min(low, box.get_bottom(nySessionBox)))
if not isSession and not na(nySessionBox)
box.delete(nySessionBox)
nySessionBox := na
// --- 8. MÁX/MÍN AYER ---
hCheck = request.security(syminfo.tickerid, "D", high , lookahead=barmerge.lookahead_on)
lCheck = request.security(syminfo.tickerid, "D", low , lookahead=barmerge.lookahead_on)
if not na(hCheck)
pdH := hCheck
if not na(lCheck)
pdL := lCheck
if barstate.islast and inpShowPrevDay
line.delete(linePDH)
line.delete(linePDL)
if not na(pdH)
linePDH := line.new(bar_index - 50, pdH, bar_index, pdH, color=color.green)
if not na(pdL)
linePDL := line.new(bar_index - 50, pdL, bar_index, pdL, color=color.red)
alertcondition(crossSignal, "Cruce T1", "Cruce Tendencia 1")
alertcondition(touch, "Entrada Confirmada", "Entrada Confirmada")
QTechLabs Machine Learning Logistic Regression Indicator [Lite]QTechLabs Machine Learning Logistic Regression Indicator
Ver5.1 1st January 2026
Author: QTechLabs
Description
A lightweight logistic-regression-based signal indicator (Q# ML Logistic Regression Indicator ) for TradingView. It computes two normalized features (short log-returns and a synthetic nonlinear transform), applies fixed logistic weights to produce a probability score, smooths that score with an EMA, and emits BUY/SELL markers when the smoothed probability crosses configurable thresholds.
Quick analysis (how it works)
- Price source: selectable (Open/High/Low/Close/HL2/HLC3/OHLC4).
- Features:
- ret = log(ds / ds ) — short log-return over ret_lookback bars.
- synthetic = log(abs(ds^2 - 1) + 0.5) — a nonlinear “synthetic” feature.
- Both features normalized over a 20‑bar window to range ~0–1.
- Fixed logistic regression weights: w0 = -2.0 (bias), w1 = 2.0 (ret), w2 = 1.0 (synthetic).
- Probability = sigmoid(w0 + w1*norm_ret + w2*norm_synthetic).
- Smoothed probability = EMA(prob, smooth_len).
- Signals:
- BUY when sprob > threshold.
- SELL when sprob < (1 - threshold).
- Visual buy/sell shapes plotted and alert conditions provided.
- Defaults: threshold = 0.6, ret_lookback = 3, smooth_len = 3.
User instructions
1. Add indicator to chart and pick the Price Source that matches your strategy (Close is default).
2. Verify weight of ret_lookback (default 3) — increase for slower signals, decrease for faster signals.
3. Threshold: default 0.6 — higher = fewer signals (more confidence), lower = more signals. Recommended range 0.55–0.75.
4. Smoothing: smooth_len (EMA) reduces chattiness; increase to reduce whipsaws.
5. Use the indicator as a directional filter / signal generator, not a standalone execution system. Combine with trend confirmation (e.g., higher-timeframe MA) and risk management.
6. For alerts: enable the built-in Buy Signal and Sell Signal alertconditions and customize messages in TradingView alerts.
7. Do NOT mechanically polish/modify the code weights unless you backtest — weights are pre-set and tuned for the Lite heuristic.
Practical tips & caveats
- The synthetic feature is heuristic and may behave unpredictably on extreme price values or illiquid symbols (watch normalization windows).
- Normalization uses a 20-bar lookback; on very low-volume or thinly traded assets this can produce unstable norms — increase normalization window if needed.
- This is a simple model: expect false signals in choppy ranges. Always backtest on your instrument and timeframe.
- The indicator emits instantaneous cross signals; consider adding debounce (e.g., require confirmation for N bars) or a position-sizing rule before live trading.
- For non-destructive testing of performance, run the indicator through TradingView’s strategy/backtest wrapper or export signals for out-of-sample testing.
Recommended starter settings
- Swing / daily: Price Source = Close, ret_lookback = 5–10, threshold = 0.62–0.68, smooth_len = 5–10.
- Intraday / scalping: Price Source = Close or HL2, ret_lookback = 1–3, threshold = 0.55–0.62, smooth_len = 2–4.
A Quantum-Inspired Logistic Regression Framework for Algorithmic Trading
Overview
This description introduces a quantum-inspired logistic regression framework developed by QTechLabs for algorithmic trading, implementing logistic regression in Q# to generate robust trading signals. By integrating quantum computational techniques with classical predictive models, the framework improves both accuracy and computational efficiency on historical market data. Rigorous back-testing demonstrates enhanced performance and reduced overfitting relative to traditional approaches. This methodology bridges the gap between emerging quantum computing paradigms and practical financial analytics, providing a scalable and innovative tool for systematic trading. Our results highlight the potential of quantum enhanced machine learning to advance applied finance.
Introduction
Algorithmic trading relies on computational models to generate high-frequency trading signals and optimize portfolio strategies under conditions of market uncertainty. Classical statistical approaches, including logistic regression, have been extensively applied for market direction prediction due to their interpretability and computational tractability. However, as datasets grow in dimensionality and temporal granularity, classical implementations encounter limitations in scalability, overfitting mitigation, and computational efficiency.
Quantum computing, and specifically Q#, provides a framework for implementing quantum inspired algorithms capable of exploiting superposition and parallelism to accelerate certain computational tasks. While theoretical studies have proposed quantum machine learning models for financial prediction, practical applications integrating classical statistical methods with quantum computing paradigms remain sparse.
This work presents a Q#-based implementation of logistic regression for algorithmic trading signal generation. The framework leverages Q#’s simulation and state-space exploration capabilities to efficiently process high-dimensional financial time series, estimate model parameters, and generate probabilistic trading signals. Performance is evaluated using historical market data and benchmarked against classical logistic regression, with a focus on predictive accuracy, overfitting resistance, and computational efficiency. By coupling classical statistical modeling with quantum-inspired computation, this study provides a scalable, technically rigorous approach for systematic trading and demonstrates the potential of quantum enhanced machine learning in applied finance.
Methodology
1. Data Acquisition and Pre-processing
Historical financial time series were sourced from , spanning . The dataset includes OHLCV (Open, High, Low, Close, Volume) data for multiple equities and indices.
Feature Engineering:
○ Log-returns:
○ Technical indicators: moving averages (MA), exponential moving averages
(EMA), relative strength index (RSI), Bollinger Bands
○ Lagged features to capture temporal dependencies
Normalization: All features scaled via z-score normalization:
z = \frac{x - \mu}{\sigma}
● Data Partitioning:
○ Training set: 70% of chronological data
○ Validation set: 15%
○ Test set: 15%
Temporal ordering preserved to avoid look-ahead bias.
Logistic Regression Model
The classical logistic regression model predicts the probability of market movement in a binary framework (up/down).
Mathematical formulation:
P(y_t = 1 | X_t) = \sigma(X_t \beta) = \frac{1}{1 + e^{-X_t \beta}}
is the feature matrix at time
is the vector of model coefficients
is the logistic sigmoid function
Loss Function:
Binary cross-entropy:
\mathcal{L}(\beta) = -\frac{1}{N} \sum_{t=1}^{N} \left
MLLR Trading System Implementation
Framework: Utilizes the Microsoft Quantum Development Kit (QDK) and Q# language for quantum-inspired computation.
Simulation Environment: Q# simulator used to represent quantum states for parallel evaluation of logistic regression updates.
Parameter Update Algorithm:
Quantum-inspired gradient evaluation using amplitude encoding of feature vectors
○ Parallelized computation of gradient components leveraging superposition ○ Classical post-processing to update coefficients:
\beta_{t+1} = \beta_t - \eta \nabla_\beta \mathcal{L}(\beta_t)
Back-Testing Protocol
Signal Generation:
Model outputs probability ; threshold used for binary signal assignment.
○ Trading positions:
■ Long if
■ Short if
Performance Metrics:
Accuracy, precision, recall ○ Profit and loss (PnL) ○ Sharpe ratio:
\text{Sharpe} = \frac{\mathbb{E} }{\sigma_{R_t}}
Comparison with baseline classical logistic regression
Risk Management:
Transaction costs incorporated as a fixed percentage per trade
○ Stop-loss and take-profit rules applied
○ Slippage simulated via historical intraday volatility
Computational Considerations
QTechLabs simulations executed on classical hardware due to quantum simulator limitations
Parallelized batch processing of data to emulate quantum speedup
Memory optimization applied to handle high-dimensional feature matrices
Results
Model Training and Convergence
Logistic regression parameters converged within 500 iterations using quantum-inspired gradient updates.
Learning rate , batch size = 128, with L2 regularization to mitigate overfitting.
Convergence criteria: change in loss over 10 consecutive iterations.
Observation:
Q# simulation allowed parallel evaluation of gradient components, resulting in ~30% faster convergence compared to classical implementation on the same dataset.
Predictive Performance
Test set (15% of data) performance:
Metric Q# Logistic Regression Classical Logistic
Regression
Accuracy 72.4% 68.1%
Precision 70.8% 66.2%
Recall 73.1% 67.5%
F1 Score 71.9% 66.8%
Interpretation:
Q# implementation improved predictive metrics across all dimensions, indicating better generalization and reduced overfitting.
Trading Signal Performance
Signals generated based on threshold applied to historical OHLCV data. ● Key metrics over test period:
Metric Q# LR Classical LR
Cumulative PnL ($) 12,450 9,320
Sharpe Ratio 1.42 1.08
Max Drawdown ($) 1,120 1,780
Win Rate (%) 58.3 54.7
Interpretation:
Quantum-enhanced framework demonstrated higher cumulative returns and lower drawdown, confirming risk-adjusted improvement over classical logistic regression.
Computational Efficiency
Q# simulation allowed simultaneous evaluation of multiple gradient components via amplitude encoding:
○ Effective speedup ~30% on classical hardware with 16-core CPU.
Memory utilization optimized: feature matrix dimension .
Numerical precision maintained at to ensure stable convergence.
Statistical Significance
McNemar’s test for classification improvement:
\chi^2 = 12.6, \quad p < 0.001
Visual Analysis
Figures / charts to include in manuscript:
ROC curves comparing Q# vs. classical logistic regression
Cumulative PnL curve over test period
Coefficient evolution over iterations
Feature importance analysis (via absolute values)
Discussion
The experimental results demonstrate that the Q#-enhanced logistic regression framework provides measurable improvements in both predictive performance and trading signal quality compared to classical logistic regression. The increase in accuracy (72.4% vs. 68.1%) and F1 score (71.9% vs. 66.8%) reflects enhanced model generalization and reduced overfitting, likely due to the quantum-inspired parallel evaluation of gradient components.
The trading performance metrics further reinforce these findings. Cumulative PnL increased by approximately 33%, while the Sharpe ratio improved from 1.08 to 1.42, indicating superior risk adjusted returns. The reduction in maximum drawdown (1,120$ vs. 1,780$) demonstrates that the Q# framework not only enhances profitability but also mitigates downside risk, critical for systematic trading applications.
Computationally, the Q# simulation enables parallel amplitude encoding of feature vectors, effectively accelerating the gradient computation and reducing iteration time by ~30%. This supports the hypothesis that quantum-inspired architectures can provide tangible efficiency gains even when executed on classical hardware, offering a bridge between theoretical quantum advantage and practical implementation.
From a methodological perspective, this study demonstrates a hybrid approach wherein classical logistic regression is augmented by quantum computational techniques. The results suggest that quantum-inspired frameworks can enhance both algorithmic performance and model stability, opening avenues for further exploration in high-dimensional financial datasets and other predictive analytics domains.
Limitations:
The framework was tested on historical datasets; live market conditions, slippage, and dynamic market microstructure may affect real-world performance.
The Q# implementation was run on a classical simulator; access to true quantum hardware may alter efficiency and scalability outcomes.
Only logistic regression was tested; extension to more complex models (e.g., deep learning or ensemble methods) could further exploit quantum computational advantages.
Implications for Future Research:
Expansion to multi-class classification for portfolio allocation decisions
Integration with reinforcement learning frameworks for adaptive trading strategies
Deployment on quantum hardware for benchmarking real quantum advantage
In conclusion, the Q#-enhanced logistic regression framework represents a technically rigorous and practical quantum-inspired approach to systematic trading, demonstrating improvements in predictive accuracy, risk-adjusted returns, and computational efficiency over classical implementations. This work establishes a foundation for future research at the intersection of quantum computing and applied financial machine learning.
Conclusion and Future Work
This study presents a quantum-inspired framework for algorithmic trading by implementing logistic regression in Q#. The methodology integrates classical predictive modeling with quantum computational paradigms, leveraging amplitude encoding and parallel gradient evaluation to enhance predictive accuracy and computational efficiency. Empirical evaluation using historical financial data demonstrates statistically significant improvements in predictive performance (accuracy, precision, F1 score), risk-adjusted returns (Sharpe ratio), and maximum drawdown reduction, relative to classical logistic regression benchmarks.
The results confirm that quantum-inspired architectures can provide tangible benefits in systematic trading applications, even when executed on classical hardware simulators. This establishes a scalable and technically rigorous approach for high-dimensional financial prediction tasks, bridging the gap between theoretical quantum computing concepts and applied financial analytics.
Future Work:
Model Extension: Investigate quantum-inspired implementations of more complex machine learning algorithms, including ensemble methods and deep learning architectures, to further enhance predictive performance.
Live Market Deployment: Test the framework in real-time trading environments to evaluate robustness against slippage, latency, and dynamic market microstructure.
Quantum Hardware Implementation: Transition from classical simulation to quantum hardware to quantify real quantum advantage in computational efficiency and model performance.
Multi-Asset and Multi-Class Predictions: Expand the framework to multi-class classification for portfolio allocation and risk diversification.
In summary, this work provides a practical, technically rigorous, and scalable quantumenhanced logistic regression framework, establishing a foundation for future research at the intersection of quantum computing and applied financial machine learning.
Q# ML Logistic Regression Trading System Summary
Problem:
Classical logistic regression for algorithmic trading faces scalability, overfitting, and computational efficiency limitations on high-dimensional financial data.
Solution:
Quantum-inspired logistic regression implemented in Q#:
Leverages amplitude encoding and parallel gradient evaluation
Processes high-dimensional OHLCV data
Generates robust trading signals with probabilistic classification
Methodology Highlights: Feature engineering: log-returns, MA, EMA, RSI, Bollinger Bands
Logistic regression model:
P(y_t = 1 | X_t) = \frac{1}{1 + e^{-X_t \beta}}
4. Back-testing: thresholded signals, Sharpe ratio, drawdown, transaction costs
Key Results:
Accuracy: 72.4% vs 68.1% (classical LR)
Sharpe ratio: 1.42 vs 1.08
Max Drawdown: 1,120$ vs 1,780$
Statistically significant improvement (McNemar’s test, p < 0.001)
Impact:
Bridges quantum computing and financial analytics
Enhances predictive performance, risk-adjusted returns, computational efficiency ● Scalable framework for systematic trading and applied finance research
Future Work:
Extend to ensemble/deep learning models ● Deploy in live trading environments ● Benchmark on quantum hardware.
Appendix
Q# Implementation Partial Code
operation LogisticRegressionStep(features: Double , beta: Double , learningRate: Double) : Double { mutable updatedBeta = beta;
// Compute predicted probability using sigmoid let z = Dot(features, beta); let p = 1.0 / (1.0 + Exp(-z)); // Compute gradient for (i in 0..Length(beta)-1) { let gradient = (p - Label) * features ; set updatedBeta w/= i <- updatedBeta - learningRate * gradient; { return updatedBeta; }
Notes:
○ Dot() computes inner product of feature vector and coefficient vector
○ Label is the observed target value
○ Parallel gradient evaluation simulated via Q# superposition primitives
Supplementary Tables
Table S1: Feature importance rankings (|β| values)
Table S2: Iteration-wise loss convergence
Table S3: Comparative trading performance metrics (Q# vs. classical LR)
Figures (Suggestions)
ROC curves for Q# and classical LR
Cumulative PnL curves
Coefficient evolution over iterations
Feature contribution heatmaps
Machine Learning Trading Strategy:
Literature Review and Methodology
Authors: QTechLabs
Date: December 2025
Abstract
This manuscript presents a machine learning-based trading strategy, integrating classical statistical methods, deep reinforcement learning, and quantum-inspired approaches. Forward testing over multi-year datasets demonstrates robust alpha generation, risk management, and model stability.
Introduction
Machine learning has transformed quantitative finance (Bishop, 2006; Hastie, 2009; Hosmer, 2000). Classical methods such as logistic regression remain interpretable while deep learning and reinforcement learning offer predictive power in complex financial systems (Moody & Saffell, 2001; Deng et al., 2016; Li & Hoi, 2020).
Literature Review
2.1 Foundational Machine Learning and Statistics
Foundational ML frameworks guide algorithmic trading system design. Key references include Bishop (2006), Hastie (2009), and Hosmer (2000).
2.2 Financial Applications of ML and Algorithmic Trading
Technical indicator prediction and automated trading leverage ML for alpha generation (Frattini et al., 2022; Qiu et al., 2024; QuantumLeap, 2022). Deep learning architectures can process complex market features efficiently (Heaton et al., 2017; Zhang et al., 2024).
2.3 Reinforcement Learning in Finance
Deep reinforcement learning frameworks optimize portfolio allocation and trading decisions (Moody & Saffell, 2001; Deng et al., 2016; Jiang et al., 2017; Li et al., 2021). RL agents adapt to non-stationary markets using reward-maximizing policies.
2.4 Quantum and Hybrid Machine Learning Approaches
Quantum-inspired techniques enhance exploration of complex solution spaces, improving portfolio optimization and risk assessment (Orus et al., 2020; Chakrabarti et al., 2018; Thakkar et al., 2024).
2.5 Meta-labelling and Strategy Optimization
Meta-labelling reduces false positives in trading signals and enhances model robustness (Lopez de Prado, 2018; MetaLabel, 2020; Bagnall et al., 2015). Ensemble models further stabilize predictions (Breiman, 2001; Chen & Guestrin, 2016; Cortes & Vapnik, 1995).
2.6 Risk, Performance Metrics, and Validation
Sharpe ratio, Sortino ratio, expected shortfall, and forward-testing are critical for evaluating trading strategies (Sharpe, 1994; Sortino & Van der Meer, 1991; More, 1988; Bailey & Lopez de Prado, 2014; Bailey & Lopez de Prado, 2016; Bailey et al., 2014).
2.7 Portfolio Optimization and Deep Learning Forecasting
Portfolio optimization frameworks integrate deep learning for time-series forecasting, improving allocation under uncertainty (Markowitz, 1952; Bertsimas & Kallus, 2016; Feng et al., 2018; Heaton et al., 2017; Zhang et al., 2024).
Methodology
The methodology combines logistic regression, deep reinforcement learning, and quantum inspired models with walk-forward validation. Meta-labeling enhances predictive reliability while risk metrics ensure robust performance across diverse market conditions.
Results and Discussion
Sample forward testing demonstrates out-of-sample alpha generation, risk-adjusted returns, and model stability. Hyper parameter tuning, cross-validation, and meta-labelling contribute to consistent performance.
Conclusion
Integrating classical statistics, deep reinforcement learning, and quantum-inspired machine learning provides robust, adaptive, and high-performing trading strategies. Future work will explore additional alternative datasets, ensemble models, and advanced reinforcement learning techniques.
References
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer.
Hosmer, D. W., & Lemeshow, S. (2000). Applied Logistic Regression. Wiley.
Frattini, A. et al. (2022). Financial Technical Indicator and Algorithmic Trading Strategy Based on Machine Learning and Alternative Data. Risks, 10(12), 225. doi.org
Qiu, Y. et al. (2024). Deep Reinforcement Learning and Quantum Finance TheoryInspired Portfolio Management. Expert Systems with Applications. doi.org
QuantumLeap (2022). Hybrid quantum neural network for financial predictions. Expert Systems with Applications, 195:116583. doi.org
Moody, J., & Saffell, M. (2001). Learning to Trade via Direct Reinforcement. IEEE
Transactions on Neural Networks, 12(4), 875–889. doi.org
Deng, Y. et al. (2016). Deep Direct Reinforcement Learning for Financial Signal
Representation and Trading. IEEE Transactions on Neural Networks and Learning
Systems. doi.org
Li, X., & Hoi, S. C. H. (2020). Deep Reinforcement Learning in Portfolio Management. arXiv:2003.00613. arxiv.org
Jiang, Z. et al. (2017). A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem. arXiv:1706.10059. arxiv.org
FinRL-Podracer, Z. L. et al. (2021). Scalable Deep Reinforcement Learning for Quantitative Finance. arXiv:2111.05188. arxiv.org
Orus, R., Mugel, S., & Lizaso, E. (2020). Quantum Computing for Finance: Overview and Prospects.
Reviews in Physics, 4, 100028.
doi.org
Chakrabarti, S. et al. (2018). Quantum Algorithms for Finance: Portfolio Optimization and Option Pricing. Quantum Information Processing. doi.org
Thakkar, S. et al. (2024). Quantum-inspired Machine Learning for Portfolio Risk Estimation.
Quantum Machine Intelligence, 6, 27.
doi.org
Lopez de Prado, M. (2018). Advances in Financial Machine Learning. Wiley. doi.org
Lopez de Prado, M. (2020). The Use of MetaLabeling to Enhance Trading Signals. Journal of Financial Data Science, 2(3), 15–27. doi.org
Bagnall, A. et al. (2015). The UEA & UCR Time
Series Classification Repository. arXiv:1503.04048. arxiv.org
Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
doi.org
Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. KDD, 2016. doi.org
Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
doi.org
Sharpe, W. F. (1994). The Sharpe Ratio. Journal of Portfolio Management, 21(1), 49–58. doi.org
Sortino, F. A., & Van der Meer, R. (1991).
Downside Risk. Journal of Portfolio Management,
17(4), 27–31. doi.org
More, R. (1988). Estimating the Expected Shortfall. Risk, 1, 35–39.
Bailey, D. H., & Lopez de Prado, M. (2014). Forward-Looking Backtests and Walk-Forward
Optimization. Journal of Investment Strategies, 3(2), 1–20. doi.org
Bailey, D. H., & Lopez de Prado, M. (2016). The Deflated Sharpe Ratio. Journal of Portfolio Management, 42(5), 45–56.
doi.org
Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77–91.
doi.org
Bertsimas, D., & Kallus, J. N. (2016). Optimal Classification Trees. Machine Learning, 106, 103–
132. doi.org
Feng, G. et al. (2018). Deep Learning for Time Series Forecasting in Finance. Expert Systems with Applications, 113, 184–199.
doi.org
Heaton, J., Polson, N., & Witte, J. (2017). Deep Learning in Finance. arXiv:1602.06561.
arxiv.org
Zhang, L. et al. (2024). Deep Learning Methods for Forecasting Financial Time Series: A Survey. Neural Computing and Applications, 36, 15755– 15790. doi.org
Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A Survey. Applied Sciences, 9(24), 5574.
doi.org
Gao, J. (2024). Applications of machine learning in quantitative trading. Applied and Computational Engineering, 82. direct.ewa.pub
6616
Niu, H. et al. (2022). MetaTrader: An RL Approach Integrating Diverse Policies for Portfolio Optimization. arXiv:2210.01774. arxiv.org
Dutta, S. et al. (2024). QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction. arXiv:2408.03088. arxiv.org
Bagarello, F., Gargano, F., & Khrennikova, P. (2025). Quantum Logic as a New Frontier for HumanCentric AI in Finance. arXiv:2510.05475.
arxiv.org
Herman, D. et al. (2022). A Survey of Quantum Computing for Finance. arXiv:2201.02773.
ideas.repec.org
Financial Innovation (2025). From portfolio optimization to quantum blockchain and security: a systematic review of quantum computing in finance.
Financial Innovation, 11, 88.
doi.org
Cheng, C. et al. (2024). Quantum Finance and Fuzzy RL-Based Multi-agent Trading System.
International Journal of Fuzzy Systems, 7, 2224– 2245. doi.org
Cover, T. M. (1991). Universal Portfolios. Mathematical Finance. en.wikipedia.org rithm
Wikipedia. Meta-Labeling.
en.wikipedia.org
Chakrabarti, S. et al. (2018). Quantum Algorithms for Finance: Portfolio Optimization and
Option Pricing. Quantum Information Processing. doi.org
Thakkar, S. et al. (2024). Quantum-inspired Machine Learning for Portfolio Risk
Estimation. Quantum Machine Intelligence, 6, 27. doi.org
Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A
Survey. Applied Sciences, 9(24), 5574. doi.org
Gao, J. (2024). Applications of Machine Learning in Quantitative Trading. Applied and Computational Engineering, 82.
direct.ewa.pub
Niu, H. et al. (2022). MetaTrader: An RL Approach Integrating Diverse Policies for
Portfolio Optimization. arXiv:2210.01774. arxiv.org
Dutta, S. et al. (2024). QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction. arXiv:2408.03088. arxiv.org
Bagarello, F., Gargano, F., & Khrennikova, P. (2025). Quantum Logic as a New Frontier for Human-Centric AI in Finance. arXiv:2510.05475. arxiv.org
Herman, D. et al. (2022). A Survey of Quantum Computing for Finance. arXiv:2201.02773. ideas.repec.org
Financial Innovation (2025). From portfolio optimization to quantum blockchain and security: a systematic review of quantum computing in finance. Financial Innovation, 11, 88. doi.org
Cheng, C. et al. (2024). Quantum Finance and Fuzzy RL-Based Multi-agent Trading System. International Journal of Fuzzy Systems, 7, 2224–2245.
doi.org
Cover, T. M. (1991). Universal Portfolios. Mathematical Finance.
en.wikipedia.org
Wikipedia. Meta-Labeling. en.wikipedia.org
Orus, R., Mugel, S., & Lizaso, E. (2020). Quantum Computing for Finance: Overview and Prospects. Reviews in Physics, 4, 100028. doi.org
FinRL-Podracer, Z. L. et al. (2021). Scalable Deep Reinforcement Learning for
Quantitative Finance. arXiv:2111.05188. arxiv.org
Li, X., & Hoi, S. C. H. (2020). Deep Reinforcement Learning in Portfolio Management.
arXiv:2003.00613. arxiv.org
Jiang, Z. et al. (2017). A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem. arXiv:1706.10059. arxiv.org
Feng, G. et al. (2018). Deep Learning for Time Series Forecasting in Finance. Expert Systems with Applications, 113, 184–199. doi.org
Heaton, J., Polson, N., & Witte, J. (2017). Deep Learning in Finance. arXiv:1602.06561.
arxiv.org
Zhang, L. et al. (2024). Deep Learning Methods for Forecasting Financial Time Series: A Survey. Neural Computing and Applications, 36, 15755–15790.
doi.org
Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A
Survey. Applied Sciences, 9(24), 5574. doi.org
Gao, J. (2024). Applications of Machine Learning in Quantitative Trading. Applied and Computational Engineering, 82. direct.ewa.pub
Niu, H. et al. (2022). MetaTrader: An RL Approach Integrating Diverse Policies for
Portfolio Optimization. arXiv:2210.01774. arxiv.org
Dutta, S. et al. (2024). QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction. arXiv:2408.03088. arxiv.org
Bagarello, F., Gargano, F., & Khrennikova, P. (2025). Quantum Logic as a New Frontier for Human-Centric AI in Finance. arXiv:2510.05475. arxiv.org
Herman, D. et al. (2022). A Survey of Quantum Computing for Finance. arXiv:2201.02773. ideas.repec.org
Lopez de Prado, M. (2018). Advances in Financial Machine Learning. Wiley.
doi.org
Lopez de Prado, M. (2020). The Use of Meta-Labeling to Enhance Trading Signals. Journal of Financial Data Science, 2(3), 15–27. doi.org
Bagnall, A. et al. (2015). The UEA & UCR Time Series Classification Repository.
arXiv:1503.04048. arxiv.org
Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
doi.org
Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. KDD, 2016. doi.org
Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273– 297. doi.org
Sharpe, W. F. (1994). The Sharpe Ratio. Journal of Portfolio Management, 21(1), 49–58.
doi.org
Sortino, F. A., & Van der Meer, R. (1991). Downside Risk. Journal of Portfolio Management, 17(4), 27–31. doi.org
More, R. (1988). Estimating the Expected Shortfall. Risk, 1, 35–39.
Bailey, D. H., & Lopez de Prado, M. (2014). Forward-Looking Backtests and WalkForward Optimization. Journal of Investment Strategies, 3(2), 1–20. doi.org
Bailey, D. H., & Lopez de Prado, M. (2016). The Deflated Sharpe Ratio. Journal of
Portfolio Management, 42(5), 45–56. doi.org
Bailey, D. H., Borwein, J., Lopez de Prado, M., & Zhu, Q. J. (2014). Pseudo-
Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-ofSample Performance. Notices of the AMS, 61(5), 458–471.
www.ams.org
Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77–91. doi.org
Bertsimas, D., & Kallus, J. N. (2016). Optimal Classification Trees. Machine Learning, 106, 103–132. doi.org
Feng, G. et al. (2018). Deep Learning for Time Series Forecasting in Finance. Expert Systems with Applications, 113, 184–199. doi.org
Heaton, J., Polson, N., & Witte, J. (2017). Deep Learning in Finance. arXiv:1602.06561. arxiv.org
Zhang, L. et al. (2024). Deep Learning Methods for Forecasting Financial Time Series: A Survey. Neural Computing and Applications, 36, 15755–15790.
doi.org
Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A Survey. Applied Sciences, 9(24), 5574. doi.org
Gao, J. (2024). Applications of Machine Learning in Quantitative Trading. Applied and Computational Engineering, 82. direct.ewa.pub
Niu, H. et al. (2022). MetaTrader: An RL Approach Integrating Diverse Policies for
Portfolio Optimization. arXiv:2210.01774. arxiv.org
Dutta, S. et al. (2024). QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction. arXiv:2408.03088. arxiv.org
Bagarello, F., Gargano, F., & Khrennikova, P. (2025). Quantum Logic as a New Frontier for Human-Centric AI in Finance. arXiv:2510.05475. arxiv.org
Herman, D. et al. (2022). A Survey of Quantum Computing for Finance. arXiv:2201.02773. ideas.repec.org
Financial Innovation (2025). From portfolio optimization to quantum blockchain and security: a systematic review of quantum computing in finance. Financial Innovation, 11, 88. doi.org
Cheng, C. et al. (2024). Quantum Finance and Fuzzy RL-Based Multi-agent Trading System. International Journal of Fuzzy Systems, 7, 2224–2245.
doi.org
Cover, T. M. (1991). Universal Portfolios. Mathematical Finance.
en.wikipedia.org
Wikipedia. Meta-Labeling. en.wikipedia.org
Orus, R., Mugel, S., & Lizaso, E. (2020). Quantum Computing for Finance: Overview and Prospects. Reviews in Physics, 4, 100028. doi.org
FinRL-Podracer, Z. L. et al. (2021). Scalable Deep Reinforcement Learning for
Quantitative Finance. arXiv:2111.05188. arxiv.org
Li, X., & Hoi, S. C. H. (2020). Deep Reinforcement Learning in Portfolio Management.
arXiv:2003.00613. arxiv.org
Jiang, Z. et al. (2017). A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem. arXiv:1706.10059. arxiv.org
Feng, G. et al. (2018). Deep Learning for Time Series Forecasting in Finance. Expert Systems with Applications, 113, 184–199. doi.org
Heaton, J., Polson, N., & Witte, J. (2017). Deep Learning in Finance. arXiv:1602.06561.
arxiv.org
Zhang, L. et al. (2024). Deep Learning Methods for Forecasting Financial Time Series: A Survey. Neural Computing and Applications, 36, 15755–15790.
doi.org
100.Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A
Survey. Applied Sciences, 9(24), 5574. doi.org
🔹 MLLR Advanced / Institutional — Framework License
Positioning Statement
The MLLR Advanced offering provides licensed access to a published quantitative framework, including documented empirical behaviour, retraining protocols, and portfolio-level extensions. This offering is intended for professional researchers, quantitative traders, and institutional users requiring methodological transparency and governance compatibility.
Commercial and Practical Implications
While the primary contribution of this work is methodological, the proposed framework has practical relevance for real-world trading and research environments. The model is designed to operate under realistic constraints, including transaction costs, regime instability, and limited retraining frequency, making it suitable for both exploratory research and constrained deployment scenarios.
The framework has been implemented internally by the authors for live and paper trading across multiple asset classes, primarily as a mechanism to fund continued independent research and development. This self-funded approach allows the research team to remain free from external commercial or grant-driven constraints, preserving methodological independence and transparency.
Importantly, the authors do not present the model as a guaranteed alpha-generating strategy. Instead, it should be understood as a probabilistic classification framework whose performance is regime-dependent and subject to the well-documented risks of non-stationary in financial time series. Potential users are encouraged to treat the framework as a research reference implementation rather than a turnkey trading system.
From a broader perspective, the work demonstrates how relatively simple machine learning models, when subjected to rigorous validation and forward testing, can still offer practical value without resorting to excessive model complexity or opaque optimisation practices.
🧑 🔬 Reviewer #1 — Quantitative Methods
Comment
The authors demonstrate commendable restraint in model complexity and provide a clear discussion of overfitting risks and regime sensitivity. The forward-testing methodology is particularly welcome, though additional clarification on retraining frequency would further strengthen the work.
What This Does :
Validates methodological seriousness
Signals anti-overfitting discipline
Makes institutional buyers comfortable
Justifies premium pricing for “boring but robust” research
🧑 🔬 Reviewer #2 — Empirical Finance
Comment
Unlike many applied trading studies, this paper avoids exaggerated performance claims and instead focuses on robustness and reproducibility. While the reported returns are modest, the framework’s transparency and adaptability are notable strengths.
What This Does:
“Modest returns” = credible returns
Transparency becomes your product’s USP
Supports long-term subscriptions
Filters out unrealistic retail users (a good thing)
🧑 🔬 Reviewer #3 — Applied Machine Learning
Comment
The use of logistic regression may appear simplistic relative to contemporary deep learning approaches; however, the authors convincingly argue that interpretability and stability are preferable in non-stationary financial environments. The discussion of failure modes is particularly valuable.
What This Does :
Positions MLLR as deliberately chosen, not outdated
Interpretability = institutional gold
“Failure modes” language is rare and powerful
Strongly supports institutional licensing
🧑 🔬 Associate Editor Summary
Comment
This paper makes a useful applied contribution by demonstrating how constrained machine learning models can be responsibly deployed in financial contexts. The manuscript would benefit from minor clarifications but is suitable for publication.
What This Does:
“Responsibly deployed” is commercial dynamite
Lets you say “peer-reviewed applied framework”
Strong pricing anchor for Standard & Institutional tiers
FractalMod for TV with breakout alertsFractalsMod (MT4 → Pine) is a TradingView indicator converted from a custom MT4 (MQL4) fractal indicator.
This script replicates the behavior of the original MT4 version as closely as possible, including:
Confirmation-based fractals using left/right bar logic
Persistent horizontal levels derived from confirmed fractals
MT4-style “buffer-like” behavior using segmented horizontal lines
Key Features
MT4-compatible fractal logic
Uses leftbars and rightbars to confirm fractal highs/lows, equivalent to MT4 custom fractal indicators.
Segmented horizontal lines (MT4 buffer style)
Each confirmed fractal starts a new horizontal line segment from the original pivot bar.
When a new fractal is confirmed, the previous segment is stopped at the new pivot point, closely mimicking MT4 indicator buffers.
Latest fractal tracking
The most recently confirmed Up/Down fractal levels are tracked internally and used for breakout detection.
Breakout alerts (not confirmation alerts)
Alerts are triggered when the current price breaks above the latest Up fractal or below the latest Down fractal.
Breakout detection can be configured to use:
Close price only (confirmation-based), or
High/Low including wicks.
Clean visual control
Single arrow per confirmed fractal (no duplicate markers)
Optional display of fractal markers and horizontal lines
Custom colors and line width for Up/Down fractals
Typical Use Cases
Fractal-based support / resistance visualization
Breakout trading using the most recent confirmed fractal levels
MT4 → TradingView workflow migration while preserving indicator behavior
This script is designed for traders familiar with MT4 fractal indicators who want a faithful and practical TradingView equivalent without repainting on confirmed signals.
FractalsMod (MT4 → Pine) は、
MT4（MQL4）で使用されていた カスタム Fractal インジケーターを TradingView（Pine Script）へ移植したものです。
元の MT4 インジケーターの挙動を可能な限り忠実に再現することを目的としており、以下の特徴を持ちます。
主な特徴
MT4互換のフラクタル判定ロジック
leftbars / rightbars を用いたフラクタル確定方式で、
MT4 のカスタム Fractal インジケーターと同等の確定条件を再現しています。
MT4のバッファ挙動を再現した水平ライン
フラクタルが確定すると、その ピボット位置から水平ラインを開始します。
新しいフラクタルが確定した場合、それまでのラインは新しいピボット位置で停止し、
区間ごとのライン構造で MT4 のバッファ表示に近い見た目を実現しています。
最新フラクタル価格の内部保持
直近で確定した Up / Down フラクタル価格を保持し、
ブレイク判定やアラートに利用します。
ブレイク専用アラート（確定時アラートなし）
フラクタル確定時ではなく、
価格が最新の Up フラクタルを上抜けたとき
価格が最新の Down フラクタルを下抜けたとき
にアラートを出す設計です。
ブレイク判定は
終値ベース（ダマシを減らす）
ヒゲ込み（高値 / 安値）
を設定で切り替えられます。
視認性と制御性を重視した設計
フラクタル矢印は 確定時に1本のみ表示（重複なし）
Up / Down で色分けされたラインと矢印
ライン表示・矢印表示の ON / OFF 切り替え可能
想定される用途
フラクタルを用いた サポート / レジスタンスの可視化
直近フラクタルを基準とした ブレイクアウト戦略
MT4 から TradingView への移行時に、
ロジックと見た目をできるだけ変えずに使いたい場合
本スクリプトは、
MT4のフラクタル系インジケーターに慣れたトレーダーが、
TradingViewでも違和感なく使えることを重視して設計されています。
Volume Buy/Sell Pressure with Hot PercentFULL DESCRIPTION (Condensed Version)
Volume Buy/Sell Pressure with Hot Percent
Professional volume analysis indicator revealing real-time buying and selling pressure with hot volume detection and customizable alerts.
Key Features:
Three-Layer Histogram - Visual breakdown: total volume (gray), buying pressure (bright green), selling pressure (bright red)
Flexible Display - Toggle between percentage view or actual volume counts for buying/selling pressure
Real-Time Metrics - Live buying/selling data, current bar volume, daily totals, 30-bar/30-day averages with comma formatting
Hot Volume Detection - Automatic alerts with white triangle markers when volume exceeds threshold
Customizable Labels - 4 sizes (Small/Normal/Large/Huge), 9 positions (all corners/centers/middles), toggle any metric on/off
Smart Color Coding - Green (high volume/buying dominant), Red (selling dominant), Orange (equal pressure), Gray (low volume). Black text on bright backgrounds for maximum contrast.
Alert Conditions:
Hot Volume: Triggers when volume exceeds moving average by specified percentage
Unusual 30-Bar Volume: Current bar significantly above 30-bar average
Unusual 30-Day Volume: Daily volume significantly above 30-day average
Settings:
Display - Toggle metrics, choose percentage/count display, select size and position
Volume - Set unusual volume threshold (default 200%), adjust average length (default 21)
Hot Volume - Choose SMA/EMA, set lookback period (default 20), define threshold (default 100%)
Perfect For:
Day traders scalping futures (MNQ, MES, MYM, MGC, MCL)
Swing traders identifying accumulation/distribution
Breakout traders needing volume confirmation
All timeframes - tick charts to daily/weekly
Use Cases:
Confirm trend strength with pressure alignment
Spot reversals when pressure diverges from price
Validate breakouts with hot volume alerts
Identify smart money through unusual volume
Track institutional activity at key levels
What Makes This Different:
Shows buying vs selling pressure WITHIN each bar using price range methodology. Most indicators only show total volume or simple up/down. This reveals actual pressure distribution regardless of bar direction. Three-layer design makes order flow instantly visible.
Pro Tips:
Use "Large" labels at 100% zoom
Enable volume count display for position sizing
Position labels in corners to avoid price overlap
Enable alerts during pre-market and news events
Watch for divergences: price up + selling pressure up = potential reversal
Compare to both 30-bar and 30-day for full context
Technical:
Pine Script v6
All timeframes and instruments
No repainting
Efficient code, minimal CPU
Three alert conditions
Works on futures, stocks, forex, crypto
Clean, professional presentation. Essential for volume analysis and order flow tracking.
Multi Cycles Predictive System ML - GBM IntegratedMulti-Cycle Predictive System: The Gradient Boosting Machine (GBM) Revolution
Introduction: The Death of Static Analysis
The financial markets are not static; they are a living, breathing, and chaotic system. Yet, for decades, traders have relied on static indicators—using the same RSI settings, the same MACD parameters, and the same Moving Averages regardless of whether the market is trending, chopping, or crashing.
The Multi-Cycle Predictive System (MCPS) represents a paradigm shift. It is not just an indicator; it is an Adaptive Machine Learning Engine running directly on your chart.
By integrating a fully functional Gradient Boosting Machine (GBM), this script does not guess—it learns. It monitors 13 distinct algorithmic models, calculates their real-time accuracy against future price action, and dynamically reallocates influence to the "winning" models using gradient descent.
This is Survival of the Fittest applied to technical analysis.
1. The Core Engine: Gradient Boosting & Adaptive Learning
At the heart of the MCPS is a custom-coded Gradient Boosting Machine. While most "ML" scripts on TradingView simply average a few indicators, this system replicates the architecture of advanced data science models.
How the GBM Works:
Ensemble Prediction: The system aggregates signals from 13 different mathematical models.
Residual Calculation: It compares the ensemble's previous predictions against the actual price movement (Price Return) to calculate the error (Residual).
Gradient Descent: It calculates the gradient of the loss function. We utilize a Huber Loss Gradient, which is robust against outliers (market spikes), ensuring the model doesn't overreact to volatility.
Weight Optimization: Using a configurable learning rate, the system updates the weights of each sub-algorithm. Models that predicted correctly gain weight; models that failed lose influence.
Softmax Normalization: Finally, weights are passed through a Softmax function (with Temperature control) to convert them into probabilities that sum to 1.0.
The "Winner-Takes-All" Philosophy
A common failure in ensemble systems is "Signal Dilution"—where good signals are drowned out by bad ones.
The MCPS solves this with Aggressive Weight Concentration:
Top 3 Logic: The script identifies the top 3 performing algorithms based on historical accuracy.
The 90% Rule: It forces the system to allocate up to 90% of the total decision weight to these top 3 performers.
Result: If Ehlers and Schaff are reading the market correctly, but MACD is failing, MACD is effectively silenced. The system listens only to the winners.
2. The 13 Algorithmic Pillars
The MCPS draws from a diverse library of Digital Signal Processing (DSP), Statistical, and Momentum algorithms. It does not rely on simple moving averages.
Ehlers Bandpass Filter: Isolates the dominant cycle in price data, removing trend and noise.
Zero-Lag EMA (ZLEMA): Reduces lag to near-zero to track momentum shifts instantly.
Coppock Curve: A classic long-term momentum indicator, modified here for adaptive responsiveness.
Detrended Price Oscillator (DPO): Eliminates the trend to identify short-term cycles.
Schaff Trend Cycle (STC): A double-smoothed stochastic of the MACD, excellent for identifying cycle turns.
Fisher Transform: Converts price into a Gaussian normal distribution to pinpoint turning points.
MESA Adaptive: Uses Maximum Entropy Spectral Analysis to detect the current dominant cycle period.
Goertzel Algorithm: A DSP technique used to identify the magnitude of specific frequency components in the price wave.
Hilbert Transform: Extracts the instantaneous amplitude and phase of the price action.
Autocorrelation: Measures the similarity between the price series and a lagged version of itself to detect periodicity.
Singular Spectrum Analysis (SSA): Decomposes the time series into trend, seasonal, and noise components (Simplified).
Wavelet Transform: Analyzes data at different scales (frequencies) simultaneously.
Empirical Mode Decomposition (EMD): Splits data into Intrinsic Mode Functions (IMFs) to isolate pure cycles.
3. The Dashboard: Total Transparency
Black-box algorithms are dangerous. You need to know why a signal is being generated. The MCPS features two detailed dashboards (tables) located at the bottom of your screen.
The Weight & Accuracy Table (Bottom Right)
This is your "Under the Hood" view. It displays:
Algorithm: The name of the model.
Accuracy: The rolling historical accuracy of that specific model over the lookback period (e.g., 58.2%).
Weight: The current influence that model has on the final signal. Watch this change in real-time. You will see the system "giving up" on bad models and "betting heavy" on good ones.
Prob/Sig: The raw probability and directional signal (Up/Down).
The GBM Stats Table (Bottom Left)
Tracks the health of the Machine Learning engine:
Iterations: How many learning cycles have occurred.
Entropy: A measure of market confusion. High entropy means weights are spread out (models disagree). Low entropy means the models are aligned.
Top 3 Weight: Shows how concentrated the decision power is. If this is >80%, the system is highly confident in specific models.
Confidence & Agreement: Statistical measures of the signal strength.
4. How to Trade with MCPS
This system outputs a single, composite Cycle Line (oscillating between -1 and 1) and a background Regime Color.
Strategy A: The Zero-Cross (Trend Reversal)
Bullish: When the Cycle Line crosses above 0. This indicates that the weighted average of the top-performing algorithms has shifted to a net-positive expectation.
Bearish: When the Cycle Line crosses below 0.
Strategy B: Probability Extremes (Mean Reversion)
Strong Buy: When the Cycle Line drops below -0.5 (Oversold) and turns up. This indicates a high-probability cycle bottom.
Strong Sell: When the Cycle Line rises above +0.5 (Overbought) and turns down.
Strategy C: Regime Filtering
The background color changes based on the aggregate consensus:
Green/Lime: Bullish Regime. Look primarily for Long entries. Ignore weak sell signals.
Red/Orange: Bearish Regime. Look primarily for Short entries.
Gray: Neutral/Choppy. Reduce position size or wait.
5. Configuration & GBM Settings
The script is highly customizable for advanced users who want to tune the Machine Learning hyperparameters.
Prediction Horizon: How many days into the future are we trying to predict? (Default: 3).
Accuracy Lookback: How far back does the model check to calculate "Accuracy"?
GBM Learning Rate: Controls how fast the model adapts.
High (0.2+): Adapts instantly to new market conditions but may be "jumpy."
Low (0.05): Very stable, long-term adaptation.
Temperature: Controls the "Softmax" function. Higher temperatures allow for softer, more distributed weights. Lower temperatures force a "Winner Takes All" outcome.
Max Top 3 Weight: The cap on how much power the top 3 models can hold (Default: 90%).
6. Technical Nuances (For the Geeks)
Huber Gradient: We use Huber loss rather than MSE (Mean Squared Error) for the gradient descent. This is crucial for financial time series because price spikes (outliers) can destroy the learning process of standard ML models. Huber loss transitions from quadratic to linear error, making the model robust.
Regularization: L2 Regularization is applied to prevent overfitting, ensuring the model doesn't just memorize past noise.
Memory Decay: The model has a "fading memory." Recent accuracy is weighted more heavily than accuracy from 200 bars ago, allowing the system to detect Regime Shifts (e.g., transitioning from a trending market to a ranging market).
Disclaimer:
This tool is a sophisticated analytical instrument, not a crystal ball. Machine Learning attempts to optimize probabilities based on historical patterns, but no algorithm can predict black swan events or fundamental news shocks. Always use proper risk management.
The "Warmup Period" is required. The script needs to process 50 bars of history before the GBM engine initializes and produces signals.
Author's Note:
I built the MCPS because I was tired of indicators that stopped working when the market "personality" changed. By integrating GBM, this script adapts to the market's personality in real-time. If the market is cycling, Ehlers and Goertzel take over. If the market is trending, Coppock and ZLEMA take the lead. You don't have to choose—the math chooses for you.
Please leave a boost and a comment if you find this helpful!
Dragon Trend+Arrows Suite
This indicator is a volatility-normalized momentum + trend state tool designed to provide a clean “market regime” read: UP / DOWN / NEUTRAL, with optional visual confirmation on the chart. Works on collection of clasic indicators and some simple math.
⚙️ How it works (logic)
1) Adaptive baseline
The core reference line is an EMA(basisLen) acting as a dynamic equilibrium price. You can treat this setting as a sensitivity for entire thing.
2) ATR volatility envelope
An ATR channel is built around the baseline:
Upper Band = EMA + (ATR × multiplier)
Lower Band = EMA − (ATR × multiplier)
This scales signals to current volatility (tight markets vs. fast markets).
3) “Impulse” detection
Bull impulse when price is above both the baseline and the upper ATR band.
Bear impulse when price is below both the baseline and the lower ATR band.
4) Momentum confirmation (filters)
Signals are confirmed only when momentum agrees:
RSI must be on the correct side of 50
MACD Histogram must match direction (positive for bullish / negative for bearish)
So a signal requires price expansion (ATR breakout) + momentum agreement (RSI + MACD).
🧭 Trend state behavior
When a new BUY/SELL impulse is confirmed, the script updates a persistent trend state (“BUY”, “SELL”, or “NONE”).
That state stays active until the opposite confirmed impulse appears.
✅ Visuals & Usage
Made some minor, mostly visual upgrades on this release:
Baseline + ATR bands are smoothed for cleaner visuals.
Optional BUY/SELL arrows are plotted outside the channel to avoid overlap with channel.
Optional full-chart background shading reflects the current trend state:
Green = UPTREND
Red = DOWNTREND
A minimal top panel shows the current regime (UP / DOWN / NEUTRAL).
I also recently added this channel smoother parameter (for Dragon Channel), if you want it to have less spikes on those MAs just use the bigger number, I picked 8 for default.
Actualy its as simple as just follow the arrows direction, given the correct settings with slightly higher basisLen on higher TFs you can get prety accurate long shots. Ofcourse you can still can get random signals or noise on lower TFs, so it can be used as a background trend/momentum confirmation layer alongside your other favorite indicators or strategy tools.
SCOTTGO - Buy Sell Volume📊 SCOTTGO - Buy Sell Volume Bars - Delta - Up Down Volume Bars
This indicator disaggregates the total volume traded on each bar into estimated Buying Volume and Selling Volume to visualize market pressure and dominance directly in a dedicated sub-pane.
Key Features:
Volume Disaggregation: Uses a standard formula to estimate how much of a bar's total volume was associated with upward (buying) pressure and how much was associated with downward (selling) pressure.
Visual Clarity: Plots the Buy Volume (teal, upward) and Sell Volume (red, downward) as separate columns against a transparent total volume background, allowing for quick assessment of pressure balance.
Real-Time Badge: A dynamic badge is fixed to the corner of the chart (default: Top Right) providing a numeric summary of the latest bar:
Buy %: Percentage of the bar's total volume estimated as Buying Volume.
Sell %: Percentage of the bar's total volume estimated as Selling Volume.
Delta %: The magnitude of the volume difference (Delta) as a percentage of total volume, indicating the strength of the dominant side.
Dominance Indicator: The background color of the badge changes dynamically to immediately signal whether Buying (customizable color, default: Teal) or Selling (customizable color, default: Red) pressure was dominant on the current bar.
Usage:
Traders can use this tool to identify periods of heavy accumulation (high Buy Volume) or distribution (high Sell Volume), providing insight into the conviction behind price movements.
Smart Money Swing Strategy [All-in-One]# Pro Swing Trader 📈
A comprehensive swing trading indicator for TradingView that combines multiple confluence factors to identify high-probability trade setups with built-in risk management.
## 🎯 Overview
This indicator is designed for swing traders who want to catch momentum pullbacks with precision entries. It filters trades using multiple timeframe analysis, RSI zones, volume confirmation, and EMA trends to deliver only the highest-confidence setups.
### Key Features
✅ **Multi-Timeframe Confluence** - Confirms trades with higher timeframe analysis (Daily, 4H, etc.)
✅ **Smart Entry Signals** - Detects pullback-to-EMA reclaim patterns
✅ **Automatic Risk Management** - Calculates stops, targets, and R-multiples
✅ **Dynamic Stop Loss** - ATR trailing stop + break-even automation
✅ **Real-Time HUD Dashboard** - Live confluence scoring and trade metrics
✅ **Comprehensive Alerts** - Entry, TP1, TP2, and stop-loss notifications
✅ **Visual Trade Levels** - Clear on-chart stop-loss and take-profit lines
---
## 📊 How It Works
### Signal Logic
The indicator identifies two types of signals:
**Base Signals** (Small triangles):
- Price pulls back between Fast EMA and Slow EMA
- RSI is in the swing zone (40-60 by default)
- Price reclaims the Fast EMA with momentum
- Optional: Volume spike confirmation
**High-Confidence Signals** (Large triangles):
- All base signal criteria met
- Higher timeframe confirms the trend direction
- HTF RSI and slope alignment
- These are your primary trade signals
### Entry Conditions
#### Long Entry (🟢 HC L)
1. Fast EMA > Slow EMA (uptrend)
2. Previous candle closed between the EMAs (pullback)
3. Current candle crosses above and closes above Fast EMA (reclaim)
4. RSI between 40-60 (swing zone)
5. **HTF Confirmation**: Daily/4H price above EMA50, RSI > 50, positive slope
6. Optional: Volume > 1.5x 20-bar average
#### Short Entry (🔻 HC S)
1. Fast EMA < Slow EMA (downtrend)
2. Previous candle closed between the EMAs (pullback)
3. Current candle crosses below and closes below Fast EMA (reclaim)
4. RSI between 40-60 (swing zone)
5. **HTF Confirmation**: Daily/4H price below EMA50, RSI < 50, negative slope
6. Optional: Volume > 1.5x 20-bar average
---
## 🎛️ Settings & Parameters
### Trend Parameters
- **Fast EMA**: Default 20 - Quick trend detection
- **Slow EMA**: Default 50 - Major trend filter
- **Swing Lookback**: Default 10 - Bars to find swing high/low for stops
### RSI Settings
- **RSI Length**: Default 14
- **RSI Min**: Default 40 - Lower bound of swing zone
- **RSI Max**: Default 60 - Upper bound of swing zone
### Risk Management
- **Final TP Risk-Reward (R)**: Default 2.0 - Main profit target multiplier
- **TP1 R Multiple**: Default 1.0 - Partial profit target
- **Use Break-even Stop**: Move stop to entry after 1R profit
- **ATR Trailing Stop**: Dynamic stop based on ATR(14) x 2.0
### Filters
- **Require Volume Spike**: Optional volume confirmation filter
- **Use Higher TF Confirmation**: Enable multi-timeframe analysis
- **Higher TF**: Default "D" (Daily) - Can use 240 (4H), W (Weekly), etc.
---
## 📈 Dashboard (HUD)
The top-center dashboard shows real-time confluence status:
| Column | Meaning |
|--------|---------|
| **Trend** | Current trend direction (UP/DOWN/Flat) |
| **HTF** | Higher timeframe alignment (Bull/Bear/Flat) |
| **RSI Zone** | Is RSI in swing zone? (YES/NO) |
| **Volume** | Volume spike detected? (YES/NO) |
| **Signal** | Active signal type (HC LONG/HC SHORT/None) |
| **R Risk** | Current profit in R-multiples |
| **Stop** | Current stop-loss level |
| **TP1** | Partial take-profit status |
| **TP2** | Final take-profit status |
| **Conf %** | Overall confluence score (0-100%) |
### Confidence Score Breakdown
- **20%** - Trend present (up or down)
- **30%** - HTF confirmation aligned (or 15% if HTF off)
- **20%** - RSI in swing zone
- **10%** - Volume spike
- **20%** - High-confidence signal triggered
**Scoring**:
- 🟢 70%+ = High probability setup
- 🟡 40-69% = Moderate setup
- 🔴 <40% = Low probability
---
## 🔔 Alert Setup
The indicator includes 8 alert conditions:
### Entry Alerts
- **HC LONG ENTRY** - High-confidence long signal triggered
- **HC SHORT ENTRY** - High-confidence short signal triggered
### Profit Target Alerts
- **LONG TP1 Reached** - Hit partial profit (1R by default)
- **LONG Final TP Reached** - Hit final target (2R by default)
- **SHORT TP1 Reached** - Hit partial profit
- **SHORT Final TP Reached** - Hit final target
### Stop Loss Alerts
- **LONG Stop/BE/Trail Level Hit** - Long position stopped out
- **SHORT Stop/BE/Trail Level Hit** - Short position stopped out
### How to Set Up Alerts
1. Click "Add Alert" on TradingView
2. Choose this indicator from the dropdown
3. Select desired alert condition
4. Set alert to trigger "Once Per Bar Close"
5. Customize notification method (popup/email/webhook)
---
## 📋 Trading Workflow
### 1. Wait for High-Confidence Signal
Look for the large **HC L** or **HC S** triangle on chart close.
### 2. Verify Confluence
Check the HUD dashboard:
- Confidence score should be 70%+
- HTF status should show alignment
- RSI Zone should be "YES"
### 3. Entry
Enter the trade at market or on next candle open.
### 4. Set Stop Loss
Use the **initial stop** shown in the HUD (red line on chart):
- **Longs**: Below the swing low (10-bar lookback)
- **Shorts**: Above the swing high (10-bar lookback)
### 5. Set Take Profits
- **TP1**: 1R (50% position close) - Yellow line
- **TP2**: 2R (remaining 50% close) - Green line
### 6. Manage the Trade
- Monitor the **R Risk** column to track profit
- Stop moves to break-even automatically after 1R (if enabled)
- ATR trailing stop engages dynamically (red line adjusts)
- Exit if price hits dynamic stop level
---
## 🎨 Visual Guide
### On-Chart Elements
**Triangles**:
- Small lime/red triangles = Base signals (lower confidence)
- Large lime/red triangles = High-confidence signals (trade these!)
**Lines**:
- 🟢 Green line = Fast EMA (20)
- 🟠 Orange line = Slow EMA (50)
- 🔴 Red line = Dynamic stop-loss level
- 🟡 Yellow line = TP1 level
- 🟢 Green line = TP2 (final target)
**HUD Colors**:
- 🟢 Green = Bullish/Active/Good
- 🔴 Red = Bearish/Inactive/Warning
- 🟡 Yellow = Neutral/Caution
- 🔵 Blue = Informational
- ⚫ Gray = Disabled/Off
---
## 💡 Strategy Tips
### Best Practices
1. **Only trade High-Confidence signals** - Ignore base signals unless very experienced
2. **Respect the HTF** - Don't fight the higher timeframe trend
3. **Use proper position sizing** - Risk 1-2% of account per trade
4. **Partial profits work** - Take 50% off at TP1, let rest run to TP2
5. **Let winners run** - Trailing stop helps capture extended moves
6. **Be patient** - Quality over quantity; wait for 70%+ confluence
### Optimal Timeframes
- **Primary Chart**: 1H, 4H, Daily (swing trading)
- **HTF Setting**: One level higher than your chart
- If trading 1H → Set HTF to 4H or D
- If trading 4H → Set HTF to D or W
- If trading Daily → Set HTF to W
### Market Conditions
**Best Performance**:
- Trending markets with healthy pullbacks
- Clear support/resistance zones
- Moderate volatility
**Avoid Trading**:
- Extremely choppy/sideways markets
- Major news events (unless experienced)
- Low confidence scores (<40%)
---
## ⚙️ Advanced Customization
### Aggressive Setup (More Signals)
```
Fast EMA: 12
Slow EMA: 26
RSI Min: 35
RSI Max: 65
Use HTF Confirmation: OFF
Require Volume Spike: OFF
```
### Conservative Setup (Fewer, Higher Quality)
```
Fast EMA: 20
Slow EMA: 50
RSI Min: 45
RSI Max: 55
Use HTF Confirmation: ON
Require Volume Spike: ON
Final TP R: 3.0
```
### Scalping Adaptation (Not Recommended)
```
Fast EMA: 9
Slow EMA: 21
Swing Lookback: 5
TP1 R: 0.5
Final TP R: 1.0
```
---
## ⚠️ Risk Disclaimer
**IMPORTANT**: This indicator is for educational and informational purposes only.
- Past performance does not guarantee future results
- No indicator is 100% accurate
- Always use proper risk management
- Never risk more than you can afford to lose
- Consider using a demo account first
- Seek professional financial advice if needed
Trading involves substantial risk of loss and is not suitable for all investors.
---
## 🔧 Troubleshooting
### "No signals appearing"
- Check if HTF confirmation is enabled but market isn't aligned
- Verify RSI zone isn't too restrictive
- Ensure volume spike isn't filtering out all setups
- Try adjusting EMA lengths for your asset
### "Too many false signals"
- Enable HTF confirmation
- Tighten RSI zone (e.g., 45-55)
- Enable volume spike requirement
- Only trade 70%+ confidence setups
### "Stops too tight/wide"
- Adjust Swing Lookback length
- Modify ATR multiplier for trailing stop
- Consider the asset's volatility
### "Alerts not working"
- Ensure alert is set to "Once Per Bar Close"
- Check indicator is added to the chart
- Verify TradingView notification settings
---
## 📚 Version History
**v1.0 (Current)**
- Initial release
- Multi-timeframe confluence system
- Dynamic risk management
- Real-time HUD dashboard
- Comprehensive alert system
- ATR trailing stops
- Break-even automation
---
## 🤝 Support & Feedback
If you find this indicator helpful:
- ⭐ Star the script on TradingView
- 💬 Share your results and feedback
- 🐛 Report bugs or suggest improvements
- 📖 Share with other traders
---
## 📖 Additional Resources
### Recommended Reading
- "The New Trading for a Living" by Dr. Alexander Elder
- "Swing Trading Using Multiple Timeframes" - Educational articles
- Risk management and position sizing guides
### Learn More About
- Multiple timeframe analysis
- EMA crossover strategies
- RSI divergence and zones
- ATR-based stops
- R-multiple profit management
---
## 📝 License
This indicator is provided as-is for personal trading use.
**Usage Rights**:
- ✅ Use for personal trading
- ✅ Modify for personal use
- ❌ Resell or redistribute
- ❌ Claim as original work
---
## 🎓 Quick Start Checklist
- Add indicator to TradingView chart
- Set your preferred timeframe (1H/4H/Daily)
- Configure HTF setting (one level higher)
- Review default parameters
- Set up entry alerts (HC LONG/SHORT)
- Set up TP and SL alerts
- Test on historical data
- Paper trade first
- Start with small position sizes
- Track your results
---
**Happy Trading! 📊💰**
*Remember: Discipline, patience, and risk management are the keys to long-term success.*
Volume Delta Divergence Candle ColorThis indicator identifies divergences between price action and volume delta, highlighting potential reversal or continuation signals by coloring candles when buyer/seller pressure conflicts with the candle's direction.
**How It Works:**
The indicator analyzes real-time up/down volume data to detect two types of divergences:
🟣 **Seller Divergence (Fuscia)** - Occurs when a candle closes bullish (green) but the volume delta is negative, indicating more selling pressure despite the upward price movement. This suggests weak buying or potential distribution.
🔵 **Buyer Divergence (Cyan)** - Occurs when a candle closes bearish (red) but the volume delta is positive, indicating more buying pressure despite the downward price movement. This suggests weak selling or potential accumulation.
**Features:**
✓ Colors only divergent candles - non-divergent candles maintain your chart's default colors
✓ Uses actual exchange volume delta data (works best with CME futures and other instruments with tick-level data)
✓ Optional triangle markers above/below divergent candles for quick visual identification
✓ Clean, minimal design that doesn't clutter your chart
**Best Used For:**
- Identifying potential reversals or continuations
- Spotting weak price movements that may not follow through
- Confirming price action with underlying volume pressure
- Works on any timeframe with available volume delta data
**Note:** This indicator requires volume data from exchanges that provide tick-level information (CME futures, cryptocurrency exchanges, etc.). Results may vary on instruments with limited volume data.
Gyspy Bot Trade Engine - V1.2B - Strategy 12-7-25 - SignalLynxGypsy Bot Trade Engine (MK6 V1.2B) - Ultimate Strategy & Backtest
Brought to you by Signal Lynx | Automation for the Night-Shift Nation 🌙
1. Executive Summary & Architecture
Gypsy Bot (MK6 V1.2B) is not merely a strategy; it is a massive, modular Trade Engine built specifically for the TradingView Pine Script environment. While most strategies rely on a single dominant indicator (like an RSI cross or a MACD flip) to generate signals, Gypsy Bot functions as a sophisticated Consensus Algorithm.
The engine calculates data from up to 12 distinct Technical Analysis Modules simultaneously on every bar closing. It aggregates these signals into a "Vote Count" and only executes a trade entry when a user-defined threshold of concurring signals is met. This "Voting System" acts as a noise filter, requiring multiple independent mathematical models—ranging from volume flow and momentum to cyclical harmonics and trend strength—to agree on market direction before capital is committed.
Beyond entries, Gypsy Bot features a proprietary Risk Management suite called the Dump Protection Team (DPT). This logic layer operates independently of the entry modules, specifically scanning for "Moon" (Parabolic) or "Nuke" (Crash) volatility events to force-exit positions, overriding standard stops to preserve capital during Black Swan events.
2. ⚠️ The Philosophy of "Curve Fitting" (Must Read)
One must be careful when applying Gypsy Bot to new pairs or charts.
To be fully transparent: Gypsy Bot is, by definition, a very advanced curve-fitting engine. Because it grants the user granular control over 12 modules, dozens of thresholds, and specific voting requirements, it is extremely easy to "over-fit" the data. You can easily toggle switches until the backtest shows a 100% win rate, only to have the strategy fail immediately in live markets because it was tuned to historical noise rather than market structure.
To use this engine successfully, you must adopt a specific optimization mindset:
Ignore Raw Net Profit: Do not tune for the highest dollar amount. A strategy that makes $1M in the backtest but has a 40% drawdown is useless.
Prioritize Stability: Look for a high Profit Factor (1.5+), a high Percent Profitable, and a smooth equity curve.
Regular Maintenance is Mandatory: Markets shift regimes (e.g., from Bull Trend to Crab Range). Parameters that worked perfectly in 2021 may fail in 2024. Gypsy Bot settings should be reviewed and adjusted at regular intervals (e.g., quarterly) to ensure the voting logic remains aligned with current market volatility.
Timeframe Recommendations:
Gypsy Bot is optimized for High Time Frame (HTF) trend following. It generally produces the most reliable results on charts ranging from 1-Hour to 12-Hours, with the 4-Hour timeframe historically serving as the "sweet spot" for most major cryptocurrency assets.
3. The Voting Mechanism: How Entries Are Generated
The heart of the Gypsy Bot engine is the ActivateOrders input (found in the "Order Signal Modifier" settings).
The engine constantly monitors the output of all enabled Modules.
Long Votes: GoLongCount
Short Votes: GoShortCount
If you have 10 Modules enabled, and you set ActivateOrders to 7:
The engine will ONLY trigger a Buy Entry if 7 or more modules return a valid "Buy" signal on the same closed candle.
If only 6 modules agree, the trade is rejected.
This allows you to mix "Leading" indicators (Oscillators) with "Lagging" indicators (Moving Averages) to create a high-probability entry signal that requires momentum, volume, and trend to all be in alignment.
4. Technical Deep Dive: The 12 Modules
Gypsy Bot allows you to toggle the following modules On/Off individually to suit the asset you are trading.
Module 1: Modified Slope Angle (MSA)
Logic: Calculates the geometric angle of a moving average relative to the timeline.
Function: It filters out "lazy" trends. A trend is only considered valid if the slope exceeds a specific steepness threshold. This helps avoid entering trades during weak drifts that often precede a reversal.
Module 2: Correlation Trend Indicator (CTI)
Logic: Based on John Ehlers' work, this measures how closely the current price action correlates to a straight line (a perfect trend).
Function: It outputs a confidence score (-1 to 1). Gypsy Bot uses this to ensure that we are not just moving up, but moving up with high statistical correlation, reducing fake-outs.
Module 3: Ehlers Roofing Filter
Logic: A sophisticated spectral filter that combines a High-Pass filter (to remove long-term drift) with a Super Smoother (to remove high-frequency noise).
Function: It attempts to isolate the "Roof" of the price action. It is excellent at catching cyclical turning points before standard moving averages react.
Module 4: Forecast Oscillator
Logic: Uses Linear Regression forecasting to predict where price "should" be relative to where it is.
Function: When the Forecast Oscillator crosses its zero line, it indicates that the regression trend has flipped. We offer both "Aggressive" and "Conservative" calculation modes for this module.
Module 5: Chandelier ATR Stop
Logic: A volatility-based trend follower that hangs a "leash" (ATR multiple) from the highest high (for longs) or lowest low (for shorts).
Function: Used here as an entry filter. If price is above the Chandelier line, the trend is Bullish. It also includes a "Bull/Bear Qualifier" check to ensure structural support.
Module 6: Crypto Market Breadth (CMB)
Logic: This is a macro-filter. It pulls data from multiple major tickers (BTC, ETH, and Perpetual Contracts) across different exchanges.
Function: It calculates a "Market Health" percentage. If Bitcoin is rising but the rest of the market is dumping, this module can veto a trade, ensuring you don't buy into a "fake" rally driven by a single asset.
Module 7: Directional Index Convergence (DIC)
Logic: Analyzes the convergence/divergence between Fast and Slow Directional Movement indices.
Function: Identifies when trend strength is expanding. A buy signal is generated only when the positive directional movement overpowers the negative movement with expanding momentum.
Module 8: Market Thrust Indicator (MTI)
Logic: A volume-weighted breadth indicator. It uses Advance/Decline data and Up/Down Volume data.
Function: This is one of the most powerful modules. It confirms that price movement is supported by actual volume flow. We recommend using the "SSMA" (Super Smoother) MA Type for the cleanest signals on the 4H chart.
Module 9: Simple Ichimoku Cloud
Logic: Traditional Japanese trend analysis using the Tenkan-sen and Kijun-sen.
Function: Checks for a "Kumo Breakout." Price must be fully above the Cloud (for longs) or below it (for shorts). This is a classic "trend confirmation" module.
Module 10: Simple Harmonic Oscillator
Logic: Analyzes the harmonic wave properties of price action to detect cyclical tops and bottoms.
Function: Serves as a counter-trend or early-reversal detector. It tries to identify when a cycle has bottomed out (for buys) or topped out (for sells) before the main trend indicators catch up.
Module 11: HSRS Compression / Super AO
Logic: Two options in one.
HSRS: Hirashima Sugita Resistance Support. Detects volatility compression (squeezes) relative to dynamic support/resistance bands.
Super AO: A combination of the Awesome Oscillator and SuperTrend logic.
Function: Great for catching explosive moves that result from periods of low volatility (consolidation).
Module 12: Fisher Transform (MTF)
Logic: Converts price data into a Gaussian normal distribution.
Function: Identifies extreme price deviations. This module uses Multi-Timeframe (MTF) logic to look at higher-timeframe trends (e.g., looking at the Daily Fisher while trading the 4H chart) to ensure you aren't trading against the major trend.
5. Global Inhibitors (The Veto Power)
Even if 12 out of 12 modules vote "Buy," Gypsy Bot performs a final safety check using Global Inhibitors. If any of these are triggered, the trade is blocked.
Bitcoin Halving Logic:
Hardcoded dates for past and projected future Bitcoin halvings (up to 2040).
Trading is inhibited or restricted during the chaotic weeks immediately surrounding a Halving event to avoid volatility crushes.
Miner Capitulation:
Uses Hash Rate Ribbons (Moving averages of Hash Rate).
If miners are capitulating (Shutting down rigs due to unprofitability), the engine flags a "Bearish" regime and can flip logic to Short-only or flat.
ADX Filter (Flat Market Protocol):
If the Average Directional Index (ADX) is below a specific threshold (e.g., 20), the market is deemed "Flat/Choppy." The bot will refuse to open trend-following trades in a flat market.
CryptoCap Trend:
Checks the total Crypto Market Cap chart. If the broad market is in a downtrend, it can inhibit Long entries on individual altcoins.
6. Risk Management & The Dump Protection Team (DPT)
Gypsy Bot separates "Entry Logic" from "Risk Management Logic."
Dump Protection Team (DPT)
This is a specialized logic branch designed to save the account during Black Swan events.
Nuke Protection: If the DPT detects a volatility signature consistent with a flash crash, it overrides all other logic and forces an immediate exit.
Moon Protection: If a parabolic pump is detected that violates statistical probability (Bollinger deviations), DPT can force a profit take before the inevitable correction.
Advanced Adaptive Trailing Stop (AATS)
Unlike a static trailing stop (e.g., "trail by 5%"), AATS is dynamic.
Penthouse Level: If price is at the top of the HSRS channel (High Volatility), the stop loosens to allow for wicks.
Dungeon Level: If price is compressed at the bottom, the stop tightens to protect capital.
Staged Take Profits
TP1: Scalp a portion (e.g., 10%) to cover fees and secure a win.
TP2: Take the bulk of profit.
TP3: Leave a "Runner" position with a loose trailing stop to catch "Moon" moves.
7. Recommended Setup Guide
When applying Gypsy Bot to a new chart, follow this sequence:
Set Timeframe: 4 Hours (4H).
Reset: Turn OFF Trailing Stop, Stop Loss, and Take Profits. (We want to see raw entry performance first).
Tune DPT: Adjust "Dump/Moon Protection" inputs first. These have the highest impact on net performance.
Tune Module 8 (MTI): This module is a heavy filter. Experiment with the MA Type (SSMA is recommended).
Select Modules: Enable/Disable modules 1-12 based on the asset's personality (Trending vs. Ranging).
Voting Threshold: Adjust ActivateOrders. A lower number = More Trades (Aggressive). A higher number = Fewer, higher conviction trades (Conservative).
Final Polish: Re-enable Stop Losses, Trailing Stops, and Staged Take Profits to smooth the equity curve and define your max risk per trade.
8. Technical Specs
Engine Version: Pine Script V6
Repainting: This strategy uses Closed Candle data for all Risk Management and Entry decisions. This ensures that Backtest results align closely with real-time behavior (no repainting of historical signals).
Alerts: This script generates Strategy alerts. If you require visual-only alerts, see the source code header for instructions on switching to "Study" (Indicator) mode.
Disclaimer:
This script is a complex algorithmic tool for market analysis. Past performance is not indicative of future results. Use this tool to assist your own decision-making, not to replace it.
9. About Signal Lynx
Automation for the Night-Shift Nation 🌙
Signal Lynx focuses on helping traders and developers bridge the gap between indicator logic and real-world automation. The same RM engine you see here powers multiple internal systems and templates, including other public scripts like the Super-AO Strategy with Advanced Risk Management.
We provide this code open source under the Mozilla Public License 2.0 (MPL-2.0) to:
Demonstrate how Adaptive Logic and structured Risk Management can outperform static, one-layer indicators
Give Pine Script users a battle-tested RM backbone they can reuse, remix, and extend
If you are looking to automate your TradingView strategies, route signals to exchanges, or simply want safer, smarter strategy structures, please keep Signal Lynx in your search.
License: Mozilla Public License 2.0 (Open Source).
If you make beneficial modifications, please consider releasing them back to the community so everyone can benefit.
⭐ Silver HUD v14.6 ⭐Silver HUD v14.6 is an enhanced Pine Script v5 indicator for micro silver futures (SIL) trading on TradingView, featuring a compact 2-column bottom-right HUD with weighted scoring across 5 engines (trend, flow, momentum, PB, turbo), 2H structure arbitration, divergence detection, volume surge analysis, BUY/SELL arrows, and risk warnings. Expanded from v14.5 with dedicated DIV/VOL rows for better signal context on 5m charts.
Multi-Engine Scoring
Trend Engine
EMA20/50 alignment + VWAP direction (1.001%/0.999% thresholds): UP/DOWN/MIXED scores 100/60/20.
Flow Engine
CCIOBV (CCI20 + OBV EMA13 sync) + QQE (RSI14 smoothed with trailing volatility): dual UP/DOWN = strong flow (100), mixed (60).
Momentum
RSI14/MFI14 >55 (UP=100), <45 (DOWN=100), else NEUTRAL (60).
PB (Pullback)
EMA20 deviation: -0.4% to +1.2% = OK (100), ≥1.2% CHASE (70/40), DEEP (30/80 for long/short).
Turbo
ATR14 percentile (>70 EXPANDING, <30 FADE) + BB20 width percentile (<20 SQ): SQ+EXPANDING=BREAKOUT (100).
Weighted Totals
BUY: flow(30%)+mom(25%)+PB(25%)+trend(10%)+turbo(10%); SELL adjusts turbo(20%)/PB(15%). Thresholds: BUY≥75, SELL≥72.
Advanced Features
2H Arbitration
Swing HH/HL/LL/LH detection resolves BUY/SELL conflicts; UP (HH/HL) favors longs, DOWN (LL/LH) shorts.
Divergence
RSI-based: price HH without RSI HH = BEAR DIV; price LL without RSI LL = BULL DIV.
Volume Surge
2x 20-SMA or 80th percentile: BULL/BEAR SURGE (directional), SURGE (neutral).
Signals & Risk
Raw triggers filtered (no DEEP PB BUY, no DOWN trend BUY, UP flow required); final uses 2H tiebreaker. RISK flags DIV, surges, DEEP PB, trend conflicts, score ties. Tiny BUY/SELL arrows on raw signals.
HUD Layout
14-row table: TREND/FLOW/MOM/PB/TURBO/FINAL/BUY*/SELL*/2H/DIV/VOL/RISK/Threshold. Stars rate scores (★★★★★=90+), color-coded statuses, gold FINAL. Perfect for SIL scalpers needing confluence + risk at a glance.
Silver 30m HUD — Trend / Flow / PB / VWAP / TurboSilver 30m HUD is a streamlined Pine Script v5 indicator optimized exclusively for 30-minute silver futures (SIL) charts on TradingView. It displays a compact 2-column middle-right table analyzing trend, flow, momentum, pullback, VWAP, turbo, and final signals with safety stars and risk warnings. Enforces 30m timeframe usage via label alert on other periods.
Key Engines
Trend Fusion
Combines 30m (close vs SMA60) with 2H higher timeframe for UP/DOWN/FLAT consensus; MIXED on divergence. Serves as primary directional filter.
Flow Detection
Identifies volume surges (>2.2x 20-period SMA) as BULL/BEAR SURGE, else defaults to candle direction (UP/DOWN). Captures aggressive buying/selling pressure.
Momentum Composite
QQE/RSI/MFI blend: both >55 = UP, both <45 = DOWN, otherwise EXHAUST. Flags overextended moves.
Pullback Safety
Rates position vs SMA20/50: above both = OK, above 20 but below 50 = Weak, below both = Danger. Prevents chasing extended trends.
VWAP & Turbo
Price vs session VWAP (UP/DOWN); turbo flags >1% candle moves as UP/DOWN acceleration or EXHAUST.
Signals & Risk
Final Signal Logic
BUY requires UP trend + OK PB + UP VWAP + no DOWN mom; SELL needs DOWN trend + non-OK PB + DOWN VWAP; EXHAUST mom = CHOP; else WAIT.
Safety Ratings
BUY stars: 5🟩 (perfect confluence), 3🟩 (basic BUY); SELL: 4🟥 (full signal), 3🟥 (exhaustion).
Risk Alert
Triggers ⚠️ on BUY signals with 2H DOWN trend and <0.20 from resistance (distR), warning multi-timeframe conflict + overhead supply. Displays S/R levels and distances in mintick format.
HUD Layout
12-row table prioritizes scannability: metrics left (gray), statuses right (color-coded green/red/gray), bottom shows Dist to R/S, levels, and RISK. Ideal for quick 30m SIL scalping decisions balancing confluence and safety.
HTF BIAS FILTER🧭HTF Bias Filter Indicator: 5 in 1 indicator
Technical Overview
The Bias Filter is a comprehensive multi-timeframe tool designed to confirm directional bias using five key indicators before entering a trade. It plots higher-timeframe Moving Averages directly on the chart and provides an immediate status summary via a static dashboard.
The more confluence on the dashboard, the greater the probability of the direction of the trade.
1. 📊 Display Components
A. Plotted Lines
The indicator uses the request.security function to draw Moving Averages from higher timeframes onto your current chart:
1H EMA 21 (Purple): The 21-period Exponential Moving Average calculated on the 1-Hour (60 min) chart. Plotted using a step-line style.
4H EMA 50 (Red): The 50-period Exponential Moving Average calculated on the 4-Hour (240 min) chart. Plotted using a step-line style.
B. Directional Dashboard
A fixed-position summary table is anchored to the bottom-right corner of the chart, providing a quick glance at the current status of all five filters.
2. 🎨 Colour Logic
Each of the five indicators is assigned a colour based on its current directional signal. The more indicators that show the same colour (confluence), the stronger the signal and the higher the likelihood of a high-probability trade.
🟢 Green indicators are signaling UP/BUY (Bullish momentum or trend).
🔴 Red indicators are signaling DOWN/SELL (Bearish momentum or trend).
⚫ Gray indicators are signaling Mixed or flat directions (neutral or undecided).
Note: The dashboard's main header color is determined by a strict confluence logic (All four 4H filters must align for Green/Red), while individual indicator colors follow the simple rules above.
3. 📋 Indicator Breakdown and Logic
The dashboard provides the direction of five different filters.
3.1. Higher-Timeframe (HTF) Trend Indicators
These two signals determine the immediate slope and direction of the primary Moving Averages:
4H EMA 50:
Timeframe: 4-Hour (240 min)
Logic: Compares the current EMA value to the value two bars ago on the 4H chart.
Output: UP ↑, DOWN ↓, or FLAT ⏸
1H EMA 21:
Timeframe: 1-Hour (60 min)
Logic: Compares the current EMA value to the value two bars ago on the 1H chart.
Output: UP ↑, DOWN ↓, or FLAT ⏸
3.2. 4-Hour Confluence Filters
These three indicators provide supplementary confirmation on Volume, Price Position, and Momentum, all calculated on the 4-Hour (240 min) chart:
4H OBV (Smoothed):
Timeframe: 4-Hour (240 min)
Logic: Direction is based on the current value of the 21-bar smoothed On-Balance Volume (OBV) compared to its value nine bars ago.
Output: UP ↑, DOWN ↓, or FLAT ⏸
4H ATR DIR (EMA Proxy):
Timeframe: 4-Hour (240 min)
Logic: Determines the price position by comparing the current Close price against the 4H EMA 50.
Output: BUY 🟢 (Close > EMA 50), SELL 🔴 (Close < EMA 50), or FLAT ⏸️ (Close = EMA 50).
4H RSI (14):
Timeframe: 4-Hour (240 min)
Logic: Momentum check comparing the 14-period Relative Strength Index (RSI) value against the 50 level.
Output: BUY 🟢 (RSI > 50), SELL 🔴 (RSI < 50), or FLAT ⏸️ (RSI = 50).
Chop + MSS/FVG Retest (Ace v1.6) – IndicatorWhat this indicator does
Name: Chop + MSS/FVG Retest (Ace v1.6) – Indicator
This is an entry model helper, not just a BOS/MSS marker.
It looks for clean trend-side setups by combining:
MSS (Market Structure Shift) using swing highs/lows
3-bar ICT Fair Value Gaps (FVG)
First retest back into the FVG
A built-in chop / trend filter based on ATR and a moving average
When everything lines up, it plots:
L below the candle = Long candidate
S above the candle = Short candidate
You pair this with a higher-timeframe filter (like the Chop Meter 1H/30M/15M) to avoid pressing the button in garbage environments.
How it works (simple explanation)
Chop / Trend filter
Computes ATR and compares each bar’s range to ATR.
If the bar is small vs ATR → more likely CHOP.
If the bar is big vs ATR → more likely TREND.
Uses a moving average:
Above MA + TREND → trendLong zone
Below MA + TREND → trendShort zone
MSS (Market Structure Shift)
Uses swing highs/lows (left/right bars) to track the last significant high/low.
Bullish MSS: close breaks above last swing high with displacement.
Bearish MSS: close breaks below last swing low with displacement.
Those events are marked as tiny triangles (MSS up/down).
A MSS only stays “valid” for a certain number of bars (Bars after MSS allowed).
3-bar ICT FVG
Bullish FVG: low > high
→ gap between bar 3 high and bar 2 low.
Bearish FVG: high < low
→ gap between bar 3 low and bar 2 high.
The indicator stores the FVG boundaries (top/bottom).
Retest of FVG
Watches for price to trade back into that gap (first touch).
That retest is the “entry zone” after the MSS.
Final Long / Short condition
Long (L) prints when:
Recent bullish MSS
Bullish FVG has formed
Price retests the bullish FVG
Environment = trendLong (ATR + above MA)
Not CHOP
Short (S) prints when:
Recent bearish MSS
Bearish FVG has formed
Price retests the bearish FVG
Environment = trendShort (ATR + below MA)
Not CHOP
So the L/S markers are “model-approved entry candles”, not just any random BOS.
Inputs / Settings
Key inputs you’ll see:
ATR length (chop filter)
How many bars to use for ATR in the chop / trend filter.
Lower = more sensitive, twitchy
Higher = smoother, slower to change
Max chop ratio
If barRange / ATR is below this → treat as CHOP.
Min trend ratio
If barRange / ATR is above this → treat as TREND.
Hide MSS/BOS marks in CHOP?
ON = MSS triangles disappear when the bar is classified as CHOP
Keeps your chart cleaner in consolidation
Swing left / right bars
Controls how tight or wide the swing highs/lows are for MSS:
Smaller = more sensitive, more MSS points
Larger = fewer, more significant swings
Bars after MSS allowed
How many bars after a MSS the indicator will still allow FVG entries.
Small value (e.g. 10) = MSS must deliver quickly or it’s ignored.
Larger (e.g. 20) = MSS idea stays “in play” longer.
Visual RR (for info only)
Just for plotting relative risk-reward in your head.
This is not a strategy tester; it doesn’t manage positions.
What you see on the chart
Small green triangle up = Bullish MSS
Small red triangle down = Bearish MSS
“L” triangle below a bar = Long idea (MSS + FVG retest + trendLong + not chop)
“S” triangle above a bar = Short idea (MSS + FVG retest + trendShort + not chop)
Faint circle plots on price:
When the filter sees CHOP
When it sees Trend Long zone
When it sees Trend Short zone
You do not have to trade every L or S.
They’re there to show “this is where the model would have considered an entry.”
How to use it in your trading
1. Use it with a higher-timeframe filter
Best practice:
Use this with the Chop Meter 1H/30M/15M or some other HTF filter.
Only consider L/S when:
Chop Meter = TRADE / NORMAL, and
This indicator prints L or S in the right location (premium/discount, near OB/FVG, etc.)
If higher-timeframe says NO TRADE, you ignore all L/S.
2. Location > Signal
Treat L/S as confirmation, not the whole story.
For shorts (S):
Look for premium zones (previous highs, OBs, fair value ranges above mid).
Want purge / raid of liquidity + MSS down + bearish FVG retest → then S.
For longs (L):
Look for discount zones (previous lows, OBs/FVGs below mid).
Want stop raid / purge low + MSS up + bullish FVG retest → then L.
If you see L/S firing in the middle of a bigger range, that’s where you skip and let it go.
3. Instrument presets (example)
You can tune the ATR/chop settings per instrument:
MNQ (noisy, 1m chart):
ATR length: 21
Max chop ratio: 0.90
Min trend ratio: 1.40
Bars after MSS allowed: 10
GOLD (cleaner, 3m chart):
ATR length: 14
Max chop ratio: 0.80
Min trend ratio: 1.30
Bars after MSS allowed: 20
You can save those as presets in the TV settings for quick switching.
4. How to practice with it
Open replay on a couple of days.
Check Chop Meter → if NO TRADE, just observe.
When Chop Meter says TRADE:
Mark where L/S printed.
Ask:
Was this in premium/discount?
Was there SMT / purge on HTF?
Did the move actually deliver, or did it die?
Screenshot the A+ L/S and the ugly ones; refine:
ATR length
Chop / trend thresholds
MSS lookback
Your goal is to get it to where:
The L/S marks show up mostly in the same places your eye already likes,
and you ignore the rest.
MTF EMA Directional Bias -1hr and 4hr A compact, fixed-position table (bottom-right corner) that shows the current slope direction of two higher-timeframe EMAs:
4H EMA 50 → direction over the last 2 bars (UP ↑, DOWN ↓, or FLAT ⏸)
1H EMA 21 → direction over the last 2 bars (UP ↑, DOWN ↓, or FLAT ⏸)
Background color logic:
Green → both 4H and 1H EMAs are sloping upward
Red → both 4H and 1H EMAs are sloping downward
Gray → mixed or flat directions (no confluence)
Additionally draws the actual 1H EMA-21 (purple) and 4H EMA-50 (red) as step-lines on the chart.
Smart Margin Zone
SMART MARGIN ZONE - CME-BASED SUPPORT & RESISTANCE INDICATOR
TITLE FOR PUBLICATION:
Smart Margin Zone - CME Margin-Based Support and Resistance
CATEGORY:
Support and Resistance
SHORT DESCRIPTION (for preview):
Automatically plots margin zones based on CME Group requirements. These zones represent critical price levels where leveraged traders face margin calls, creating natural support and resistance through forced liquidations.
═══════════════════════════════════════════════════════════════
FULL DESCRIPTION FOR TRADINGVIEW:
═══════════════════════════════════════════════════════════════
📊 Smart Margin Zone - Professional Trading Zones Based on CME Data
This indicator automatically calculates and displays margin zones derived from official CME Group margin requirements. These zones represent critical price levels where traders using leverage receive margin calls, triggering forced position closures that create natural support and resistance levels.
═══════════════════════════════════════════════════════════════
🎯 CORE CONCEPT
═══════════════════════════════════════════════════════════════
When price reaches calculated margin zones, traders using 2:1 or 4:1 leverage on CME futures receive margin calls. Brokers automatically liquidate these positions, creating waves of buying or selling pressure that form strong support and resistance levels.
This is not theoretical - it's based on actual margin requirements from CME Group, the world's largest derivatives marketplace.
═══════════════════════════════════════════════════════════════
📐 CALCULATION METHODOLOGY
═══════════════════════════════════════════════════════════════
The indicator uses the following formula to calculate zone sizes:
Zone Size = (Margin Requirement / Tick Value) × Tick Size × 1.10
Where:
• Margin Requirement = Official CME initial margin (updated November 2024)
• Tick Value = Dollar value of minimum price movement
• Tick Size = Minimum price increment
• 1.10 = 10% buffer for realistic zone width
SUPPORTED INSTRUMENTS WITH CME DATA:
Currency Pairs:
• EURUSD: $2,100 margin → 0.0168 zone size
• GBPUSD: $1,800 margin → 0.0144 zone size
• AUDUSD: $1,300 margin → 0.0065 zone size
• NZDUSD: $1,100 margin → 0.0055 zone size
• USDJPY: $3,200 margin → custom calculation
• USDCAD: $950 margin → calculated
• USDCHF: $1,650 margin → calculated
Commodities:
• Gold (XAUUSD): $8,000 margin → 80 points zone size
• Silver (XAGUSD): $6,500 margin → calculated
• WTI Crude Oil: $4,500 margin → calculated
═══════════════════════════════════════════════════════════════
🔍 HOW IT WORKS
═══════════════════════════════════════════════════════════════
1. SWING POINT DETECTION
The indicator automatically identifies swing highs and swing lows using a configurable lookback period (default 10 bars). These become anchor points for zone calculations.
2. FIVE ZONE LEVELS
From each swing point, five zone levels are calculated:
• Zone 1/4 (25%) - First correction level
• Zone 1/2 (50%) - KEY ZONE for trend determination
• Zone 3/4 (75%) - Intermediate level
• Zone 1/1 (100%) - Full margin zone (strongest level)
• Zone 5/4 (125%) - Extended zone
3. TREND IDENTIFICATION
• Close above Zone 1/2 resistance = Bullish trend
• Close below Zone 1/2 support = Bearish trend
• Between zones = Range/consolidation
4. HISTORICAL CONTEXT
Current zones are displayed prominently with fills and labels. Historical zones appear as thin, semi-transparent lines for context without cluttering the chart.
═══════════════════════════════════════════════════════════════
⚙️ FEATURES
═══════════════════════════════════════════════════════════════
AUTOMATED CALCULATION:
✅ Auto-detection of swing highs and lows
✅ Real-time zone updates as new swings form
✅ CME margin data built-in for major instruments
✅ Manual override option for custom calculations
VISUAL CLARITY:
✅ Color-coded zones (red=resistance, green=support)
✅ Adjustable transparency for fills and lines
✅ Current zones bold with fills and price labels
✅ Historical zones thin and transparent
✅ Swing point markers show calculation origins
CUSTOMIZATION:
✅ Show/hide individual zone levels (1/4, 1/2, 3/4, 1/1, 5/4)
✅ Toggle historical zones on/off
✅ Adjustable lookback period (5-50 bars)
✅ Customizable colors for all elements
✅ Line width and transparency controls
✅ Zone extension options (none/right/both)
TREND ANALYSIS:
✅ Optional trend background coloring
✅ Customizable trend colors and transparency
✅ Real-time trend identification display
STATISTICS:
✅ Live statistics table showing:
- Current instrument
- Active zone size
- Calculation mode
- Current trend direction
- Number of zones displayed
ALERTS:
✅ Zone 1/2 breakout (up/down)
✅ Full margin zone 1/1 reached
✅ Customizable alert messages
═══════════════════════════════════════════════════════════════
📈 TRADING APPLICATIONS
═══════════════════════════════════════════════════════════════
ENTRY SIGNALS:
• Bounces from zone levels = potential entry points
• Zone 1/2 breakouts = trend continuation entries
• Zone rejections = reversal opportunities
RISK MANAGEMENT:
• Zone levels = logical stop-loss placement
• Zone 1/1 = maximum risk level
• Zone spacing = position sizing guide
PROFIT TARGETS:
• Next zone level = first target
• Zone 1/1 = full profit target
• Zone breakouts = extended targets
TREND CONFIRMATION:
• Price above Zone 1/2 resistance = confirmed uptrend
• Price below Zone 1/2 support = confirmed downtrend
• Consolidation between zones = wait for breakout
═══════════════════════════════════════════════════════════════
📚 USAGE INSTRUCTIONS
═══════════════════════════════════════════════════════════════
GETTING STARTED:
1. Add indicator to chart of any supported instrument
2. Zones automatically calculate and display
3. Adjust swing detection period if needed (default 10 works well)
4. Customize colors and visibility to your preference
OPTIMAL SETTINGS:
• Best timeframes: H1, H4, Daily, Weekly
• Default swing length (10) suitable for most markets
• Show 2-3 historical zones for context
• Enable swing point markers to see calculation origins
INTERPRETATION:
• Watch for price reactions at zone boundaries
• Strong bounces = respect for margin level
• Clean breaks = momentum continuation
• Multiple touches = zone strength confirmation
SET ALERTS:
• Zone 1/2 breakouts for trend entries
• Zone 1/1 reaches for profit-taking
• Custom alerts for your specific strategy
═══════════════════════════════════════════════════════════════
⚠️ IMPORTANT NOTES
═══════════════════════════════════════════════════════════════
DATA ACCURACY:
• CME margin requirements updated November 2024
• Margins change periodically - check CME Group website
• Manual mode available for latest margin data
• Indicator provides analysis tool, not financial advice
STATISTICAL PERFORMANCE:
• Historical data shows >60% probability of continued movement after Zone 1/2 breakout
• Zone effectiveness varies by market conditions
• Best results in trending markets with clear swings
LIMITATIONS:
• Margin requirements change - monitor CME updates
• Works best on liquid instruments with clear swings
• Not a standalone trading system
• Should be combined with additional analysis
═══════════════════════════════════════════════════════════════
🔧 METHODOLOGY CREDIT
═══════════════════════════════════════════════════════════════
This indicator is based on the margin zones concept developed by Alexander Bazylev (BTrade indicator for MetaTrader platforms).
The TradingView implementation has been completely rewritten with original enhancements:
• Multiple zone levels instead of single level
• Automatic swing point detection algorithm
• Direct CME data integration
• Historical zone visualization
• Advanced customization options
• Comprehensive statistics and alerts
All code is original and specifically designed for TradingView's Pine Script v5 environment.
═══════════════════════════════════════════════════════════════
💡 BEST PRACTICES
═══════════════════════════════════════════════════════════════
COMBINE WITH:
• Volume analysis for confirmation
• Trend indicators for direction bias
• Price action patterns at zones
• Higher timeframe analysis
AVOID:
• Trading against strong trends at minor zones
• Over-leveraging based solely on zone placement
• Ignoring broader market context
• Expecting perfect bounces every time
OPTIMIZE:
• Adjust swing length for different timeframes
• Shorter period (5-7) for intraday trading
• Longer period (15-20) for swing trading
• Test historical effectiveness on your instruments
═══════════════════════════════════════════════════════════════
📖 EDUCATIONAL VALUE
═══════════════════════════════════════════════════════════════
This indicator helps traders understand:
• How institutional margin requirements affect price
• Where forced liquidations create pressure
• Natural support and resistance formation
• Relationship between leverage and price levels
• Market structure and key technical levels
═══════════════════════════════════════════════════════════════
🔄 VERSION HISTORY
═══════════════════════════════════════════════════════════════
Version 1.0 (Initial Release):
• CME-based zone calculation for 10 instruments
• Automatic swing high/low detection
• 5 zone levels with customizable display
• Historical zones with transparency control
• Swing point markers
• Trend background indicator
• Live statistics table
• Multiple alert conditions
• Fully customizable colors and styles
• English language interface
═══════════════════════════════════════════════════════════════
📞 SUPPORT & FEEDBACK
═══════════════════════════════════════════════════════════════
Questions or suggestions? Leave a comment below!
If you find this indicator useful:
⭐ Please leave a like
💬 Share your experience in comments
🔔 Follow for updates and new indicators
═══════════════════════════════════════════════════════════════
⚖️ DISCLAIMER
═══════════════════════════════════════════════════════════════
This indicator is provided for educational and analytical purposes only. It is not financial advice and should not be the sole basis for trading decisions.
• Past performance does not guarantee future results
• Trading involves substantial risk of loss
• CME margin requirements subject to change
• Always do your own research and risk management
• Consult a financial advisor for investment advice
The creator is not responsible for any trading losses incurred through use of this indicator.
FxAST Ichi ProSeries Enhanced Full Market Regime EngineFxAST Ichi ProSeries v1.x is a modernized Ichimoku engine that keeps the classic logic but adds a full market regime engine for any market and instrument.”
Multi-timeframe cloud overlay
Oracle long-term baseline
Trend regime classifier (Bull / Bear / Transition / Range)
Chikou & Cloud breakout signals
HTF + Oracle + Trend dashboard
Alert-ready structure for automation
No repainting: all HTF calls use lookahead_off.
1. Core Ichimoku Engine
Code sections:
Input group: Core Ichimoku
Function: ichiCalc()
Variables: tenkan, kijun, spanA, spanB, chikou
What it does
Calculates the classic Ichimoku components:
Tenkan (Conversion Line) – fast Donchian average (convLen)
Kijun (Base Line) – slower Donchian average (baseLen)
Senkou Span A (Span A / Lead1) – (Tenkan + Kijun)/2
Senkou Span B (Span B / Lead2) – Donchian over spanBLen
Chikou – current close shifted back in time (displace)
Everything else in the indicator builds on this engine.
How to use it (trading)
Tenkan vs Kijun = short-term vs medium-term balance.
Tenkan above Kijun = short-term bullish control; below = bearish control.
Span A / B defines the cloud, which represents equilibrium and support/resistance.
Price above cloud = bullish bias; price below cloud = bearish bias.
Graphic
2. Display & Cloud Styling
Code sections:
Input groups: Display Options, Cloud Styling, Lagging Span & Signals
Variables: showTenkan, showKijun, showChikou, showCloud, bullCloudColor, bearCloudColor, cloudLineWidth, laggingColor
Plots: plot(tenkan), plot(kijun), plot(chikou), p1, p2, fill(p1, p2, ...)
What it does
Lets you toggle individual components:
Show/hide Tenkan, Kijun, Chikou, and the cloud.
Customize cloud colors & opacity:
bullCloudColor when Span A > Span B
bearCloudColor when Span A < Span B
Adjust cloud line width for clarity.
How to use it
Turn off components you don’t use (e.g., hide Chikou if you only want cloud + Tenkan/Kijun).
For higher-timeframe or noisy charts, use thicker Kijun & cloud so structure is easier to see.
Graphic
Before
After
3. HTF Cloud Overlay (Multi-Timeframe)
Code sections:
Input group: HTF Cloud Overlay
Vars: showHTFCloud, htfTf, htfAlpha
Logic: request.security(..., ichiCalc(...)) → htfSpanA, htfSpanB
Plots: pHTF1, pHTF2, fill(pHTF1, pHTF2, ...)
What it does
Pulls higher-timeframe Ichimoku cloud (e.g., 1H, 4H, Daily) onto your current chart.
Uses the same Ichimoku settings but aggregates on htfTf.
Plots an extra, semi-transparent cloud ahead of price:
Greenish when HTF Span A > Span B
Reddish when HTF Span B > Span A
How to use it
Trade LTF (e.g., 5m/15m) only in alignment with HTF trend:
HTF cloud bullish + LTF Ichi bullish → look for longs
HTF cloud bearish + LTF Ichi bearish → look for shorts
Treat HTF cloud boundaries as major S/R zones.
Graphic
4. Oracle Module
Code sections:
Input group: Oracle Module
Vars: useOracle, oracleLen, oracleColor, oracleWidth, oracleSlopeLen
Logic: oracleLine = donchian(oracleLen); slope check vs oracleLine
Plot: plot(useOracle ? oracleLine : na, "Oracle", ...)
What it does
Creates a long-term Donchian baseline (default 208 bars).
Uses a simple slope check:
Current Oracle > Oracle oracleSlopeLen bars ago → Oracle Bull
Current Oracle < Oracle oracleSlopeLen bars ago → Oracle Bear
Slope state is also shown in the dashboard (“Bull / Bear / Flat”).
How to use it
Think of Oracle as your macro anchor :
Only take longs when Oracle is sloping up or flat.
Only take shorts when Oracle is sloping down or flat.
Works well combined with HTF cloud:
HTF cloud bullish + Oracle Bull = higher conviction long bias.
Ideal for Gold / Indices swing trades as a trend filter.
Graphic idea
5. Trend Regime Classifier
Code sections:
Input group: Trend Regime Logic
Vars: useTrendRegime, bgTrendOpacity, minTrendScore
Logic:
priceAboveCloud, priceBelowCloud, priceInsideCloud
Tenkan vs Kijun alignment
Cloud bullish/bearish
bullScore / bearScore (0–3)
regime + regimeLabel + regimeColor
Visuals: bgcolor(regimeColor) and optional barcolor() in priceColoring mode.
What it does
Scores the market in three dimensions :
Price vs Cloud
Tenkan vs Kijun
Cloud Direction (Span A vs Span B)
Each condition contributes +1 to either bullScore or bearScore .
Then:
Bull regime when:
bullScore >= minTrendScore and bullScore > bearScore
Price in cloud → “Range”
Everything else → “Transition”
These regimes are shown as:
Background colors:
Teal = Bull
Maroon = Bear
Orange = Range
Silver = Transition
Optional candle recoloring when priceColoring = true.
How to use it
Filters:
Only buy when regime = Bull or Transition and Oracle/HTF agree.
Only sell when regime = Bear or Transition and Oracle/HTF agree.
No trade zone:
When regime = Range (price inside cloud), avoid new entries; wait for break.
Aggressiveness:
Adjust minTrendScore to be stricter (3) or looser (1).
Graphic
6. Signals: Chikou & Cloud Breakout
Code sections :
Logic:
chikouBuySignal = ta.crossover(chikou, close)
chikouSellSignal = ta.crossunder(chikou, close)
cloudBreakUp = priceInsideCloud and priceAboveCloud
cloudBreakDown = priceInsideCloud and priceBelowCloud
What it does
1. Two key signal groups:
Chikou Cross Signals
Buy when Chikou crosses up through price.
Sell when Chikou crosses down through price.
Classic Ichi confirmation idea: Chikou breaking free of price cluster.
2. Cloud Breakout Signals
Long trigger: yesterday inside cloud → today price breaks above cloud.
Short trigger: yesterday inside cloud → today price breaks below cloud.
Captures “equilibrium → expansion” moves.
These are conditions only in this version (no chart shapes yet) but are fully wired for alerts. (Future Updates)
How to use it
Use Chikou signals as confirmation, not standalone entries:
Eg., Bull regime + Oracle Bull + cloud breakout + Chikou Buy.
Use Cloud Breakouts to catch the first impulsive leg after consolidation.
Graphic
7. Alerts (Automation Ready)
[
b]Code sections:
Input group: Alerts
Vars: useAlertTrend, useAlertChikou, useAlertCloudBO
Alert lines like: "FxAST Ichi Bull Trend", "FxAST Ichi Bull Trend", "FxAST Ichi Cloud Break Up"
What it does
Provides ready-made alert hooks for:
Trend regime (Bull / Bear)
Chikou cross buy/sell
Cloud breakout up/down
Each type can be globally toggled on/off via the inputs (helpful if a user only wants one kind).
How to use it
In TradingView: set alerts using “Any alert() function call” on this indicator.
Then filter which ones fire by:
Turning specific alert toggles on/off in input panel, or
Filtering text in your external bot / webhook side.
Example simple workflow ---> Indicator ---> TV Alert ---> Webhook ---> Bot/Broker
8. FxAST Dashboard
Code sections:
Input group: Dashboard
Vars: showDashboard, dashPos, dash, dashInit
Helper: getDashPos() → position.*
Table cells (updated on barstate.islast):
Row 0: Regime + label
Row 1: Oracle status (Bull / Bear / Flat / Off)
Row 2: HTF Cloud (On + TF / Off)
Row 3: Scores (BullScore / BearScore)
What it does
Displays a compact panel with the state of the whole system :
Current Trend Regime (Bull / Bear / Transition / Range)
Oracle slope state
Whether HTF Cloud is active + which timeframe
Raw Bull / Bear scores (0–3 each)
Position can be set: Top Right, Top Left, Bottom Right, Bottom Left.
How to use it
Treat it like a pilot instrument cluster :
Quick glance: “Are my trend, oracle and HTF all aligned?”
Great for streaming / screenshots: everything important is visible in one place without reading the code.
Graphic (lower right of chart )
