Laguerre Moving Average Filter【Little_Turtle】===== 📊 Laguerre Filter 📊 =====
█ Overview
The Laguerre filter is a type of filter based on Laguerre polynomials, commonly used in signal processing, particularly in financial market analysis. It smooths price data to reduce noise while emphasizing trend direction. This filter provides an adaptive smoothing of price data, offering better response characteristics compared to traditional moving averages, especially when dealing with highly volatile or rapidly changing market data.
Working Principle of the Laguerre Filter The Laguerre filter is computed using Laguerre polynomials, which are recursively defined mathematical functions that progressively smooth data. By applying this polynomial, the Laguerre filter transforms input signals (such as market prices) into a smoothed signal. The degree of smoothing and the filter's responsiveness to price changes can be controlled by adjusting the parameters.
The computation process is typically performed recursively, allowing for fast calculations without directly using the explicit expression of the polynomial. This recursive approach enables the Laguerre filter to reduce noise while maintaining responsiveness to trends.
🔵 Laguerre Filter Parameters ↓↓↓
Smoothing Factor (Input Value): This parameter controls the filter's responsiveness and the degree of smoothing applied to the input signal. A smaller input value leads to stronger smoothing, while a larger input value allows the filter to respond more quickly to price changes.
🔵 Advantages of the Laguerre Filter ↓↓↓
1.Adaptability: Unlike simple moving averages (SMA) or exponential moving averages (EMA), the Laguerre filter has stronger adaptability and can automatically adjust its response speed based on market volatility.
2.Reduced Lag: Due to its recursive calculation method, the Laguerre filter reduces the lag effect commonly seen in traditional moving averages and responds faster to price changes.
3.Lower Noise: The Laguerre filter smooths out noisy signals, effectively filtering unnecessary fluctuations in the market.
🔵 Applications of the Laguerre Filter ↓↓↓
1.Trend Following: By smoothing price data, the Laguerre filter helps traders more clearly identify market trends, making it especially useful for trend-following strategies.
2.Noise Filtering: In highly volatile markets, the Laguerre filter can effectively remove random noise from price data, providing more reliable signals.
3.Indicator Support: The Laguerre filter is often used in conjunction with other technical indicators, such as RSI or MACD, to further enhance the accuracy of trading signals.
=====📄 Summary 📄=====
The Laguerre filter, through its recursive calculation method, provides a smooth and fast-responding trend filter for financial market data, reducing noise and enhancing trend identification. It is more flexible than traditional moving averages and is suitable for trend-following and noise-filtering in various financial markets.
Tradingstrategies
VIDYA ProTrend Multi-Tier ProfitHello! This time is about a trend-following system.
VIDYA is quite an interesting indicator that adjusts dynamically to market volatility, making it more responsive to price changes compared to traditional moving averages. Balancing adaptability and precision, especially with the more aggressive short trade settings, challenged me to fine-tune the strategy for a variety of market conditions.
█ Introduction and How it is Different
The "VIDYA ProTrend Multi-Tier Profit" strategy is a trend-following system that combines the VIDYA (Variable Index Dynamic Average) indicator with Bollinger Bands and a multi-step take-profit mechanism.
Unlike traditional trend strategies, this system allows for more adaptive profit-taking, adjusting for long and short positions through distinct ATR-based and percentage-based targets. The innovation lies in its dynamic multi-tier approach to profit-taking, especially for short trades, where more aggressive percentages are applied using a multiplier. This flexibility helps adapt to various market conditions by optimizing trade management and profit allocation based on market volatility and trend strength.
BTCUSD 6hr performance
█ Strategy, How it Works: Detailed Explanation
The core of the "VIDYA ProTrend Multi-Tier Profit" strategy lies in the dual VIDYA indicators (fast and slow) that analyze price trends while accounting for market volatility. These indicators work alongside Bollinger Bands to filter trade entries and exits.
🔶 VIDYA Calculation
The VIDYA indicator is calculated using the following formula:
Smoothing factor (𝛼):
alpha = 2 / (Length + 1)
VIDYA formula:
VIDYA(t) = alpha * k * Price(t) + (1 - alpha * k) * VIDYA(t-1)
Where:
k = |Chande Momentum Oscillator (MO)| / 100
🔶 Bollinger Bands as a Volatility Filter
Bollinger Bands are calculated using a rolling mean and standard deviation of price over a specified period:
Upper Band:
BB_upper = MA + (K * stddev)
Lower Band:
BB_lower = MA - (K * stddev)
Where:
MA is the moving average,
K is the multiplier (typically 2), and
stddev is the standard deviation of price over the Bollinger Bands length.
These bands serve as volatility filters to identify potential overbought or oversold conditions, aiding in the entry and exit logic.
🔶 Slope Calculation for VIDYA
The slopes of both fast and slow VIDYAs are computed to assess the momentum and direction of the trend. The slope for a given VIDYA over its length is:
Slope = (VIDYA(t) - VIDYA(t-n)) / n
Where:
n is the length of the lookback period. Positive slope indicates bullish momentum, while negative slope signals bearish momentum.
LOCAL picture
🔶 Entry and Exit Conditions
- Long Entry: Occurs when the price moves above the slow VIDYA and the fast VIDYA is trending upward. Bollinger Bands confirm the signal when the price crosses the upper band, indicating bullish strength.
- Short Entry: Happens when the price drops below the slow VIDYA and the fast VIDYA trends downward. The signal is confirmed when the price crosses the lower Bollinger Band, showing bearish momentum.
- Exit: Based on VIDYA slopes flattening or reversing, or when the price hits specific ATR or percentage-based profit targets.
🔶 Multi-Step Take Profit Mechanism
The strategy incorporates three levels of take profit for both long and short trades:
- ATR-based Take Profit: Each step applies a multiple of the ATR (Average True Range) to the entry price to define the exit point.
The first level of take profit (long):
TP_ATR1_long = Entry Price + (2.618 * ATR)
etc.
█ Trade Direction
The strategy offers flexibility in defining the trading direction:
- Long: Only long trades are considered based on the criteria for upward trends.
- Short: Only short trades are initiated in bearish trends.
- Both: The strategy can take both long and short trades depending on the market conditions.
█ Usage
To use the strategy effectively:
- Adjust the VIDYA lengths (fast and slow) based on your preference for trend sensitivity.
- Use Bollinger Bands as a filter for identifying potential breakout or reversal scenarios.
- Enable the multi-step take profit feature to manage positions dynamically, allowing for partial exits as the price reaches specified ATR or percentage levels.
- Leverage the short trade multiplier for more aggressive take profit levels in bearish markets.
This strategy can be applied to different asset classes, including equities, forex, and cryptocurrencies. Adjust the input parameters to suit the volatility and characteristics of the asset being traded.
█ Default Settings
The default settings for this strategy have been designed for moderate to trending markets:
- Fast VIDYA Length (10): A shorter length for quick responsiveness to price changes. Increasing this length will reduce noise but may delay signals.
- Slow VIDYA Length (30): The slow VIDYA is set longer to capture broader market trends. Shortening this value will make the system more reactive to smaller price swings.
- Minimum Slope Threshold (0.05): This threshold helps filter out weak trends. Lowering the threshold will result in more trades, while raising it will restrict trades to stronger trends.
Multi-Step Take Profit Settings
- ATR Multipliers (2.618, 5.0, 10.0): These values define how far the price should move before taking profit. Larger multipliers widen the profit-taking levels, aiming for larger trend moves. In higher volatility markets, these values might be adjusted downwards.
- Percentage Levels (3%, 8%, 17%): These percentage levels define how much the price must move before taking profit. Increasing the percentages will capture larger moves, while smaller percentages offer quicker exits.
- Short TP Multiplier (1.5): This multiplier applies more aggressive take profit levels for short trades. Adjust this value based on the aggressiveness of your short trade management.
Each of these settings directly impacts the performance and risk profile of the strategy. Shorter VIDYA lengths and lower slope thresholds will generate more trades but may result in more whipsaws. Higher ATR multipliers or percentage levels can delay profit-taking, aiming for larger trends but risking partial gains if the trend reverses too early.
Multi-Step FlexiSuperTrend - Strategy [presentTrading]At the heart of this endeavor is a passion for continuous improvement in the art of trading
█ Introduction and How it is Different
The "Multi-Step FlexiSuperTrend - Strategy " is an advanced trading strategy that integrates the well-known SuperTrend indicator with a nuanced and dynamic approach to market trend analysis. Unlike conventional SuperTrend strategies that rely on static thresholds and fixed parameters, this strategy introduces multi-step take profit mechanisms that allow traders to capitalize on varying market conditions in a more controlled and systematic manner.
What sets this strategy apart is its ability to dynamically adjust to market volatility through the use of an incremental factor applied to the SuperTrend calculation. This adjustment ensures that the strategy remains responsive to both minor and major market shifts, providing a more accurate signal for entries and exits. Additionally, the integration of multi-step take profit levels offers traders the flexibility to scale out of positions, locking in profits progressively as the market moves in their favor.
BTC 6hr Long/Short Performance
█ Strategy, How it Works: Detailed Explanation
The Multi-Step FlexiSuperTrend strategy operates on the foundation of the SuperTrend indicator, but with several enhancements that make it more adaptable to varying market conditions. The key components of this strategy include the SuperTrend Polyfactor Oscillator, a dynamic normalization process, and multi-step take profit levels.
🔶 SuperTrend Polyfactor Oscillator
The SuperTrend Polyfactor Oscillator is the heart of this strategy. It is calculated by applying a series of SuperTrend calculations with varying factors, starting from a defined "Starting Factor" and incrementing by a specified "Increment Factor." The indicator length and the chosen price source (e.g., HLC3, HL2) are inputs to the oscillator.
The SuperTrend formula typically calculates an upper and lower band based on the average true range (ATR) and a multiplier (the factor). These bands determine the trend direction. In the FlexiSuperTrend strategy, the oscillator is enhanced by iteratively applying the SuperTrend calculation across different factors. The iterative process allows the strategy to capture both minor and significant trend changes.
For each iteration (indexed by `i`), the following calculations are performed:
1. ATR Calculation: The Average True Range (ATR) is calculated over the specified `indicatorLength`:
ATR_i = ATR(indicatorLength)
2. Upper and Lower Bands Calculation: The upper and lower bands are calculated using the ATR and the current factor:
Upper Band_i = hl2 + (ATR_i * Factor_i)
Lower Band_i = hl2 - (ATR_i * Factor_i)
Here, `Factor_i` starts from `startingFactor` and is incremented by `incrementFactor` in each iteration.
3. Trend Determination: The trend is determined by comparing the indicator source with the upper and lower bands:
Trend_i = 1 (uptrend) if IndicatorSource > Upper Band_i
Trend_i = 0 (downtrend) if IndicatorSource < Lower Band_i
Otherwise, the trend remains unchanged from the previous value.
4. Output Calculation: The output of each iteration is determined based on the trend:
Output_i = Lower Band_i if Trend_i = 1
Output_i = Upper Band_i if Trend_i = 0
This process is repeated for each iteration (from 0 to 19), creating a series of outputs that reflect different levels of trend sensitivity.
Local
🔶 Normalization Process
To make the oscillator values comparable across different market conditions, the deviations between the indicator source and the SuperTrend outputs are normalized. The normalization method can be one of the following:
1. Max-Min Normalization: The deviations are normalized based on the range of the deviations:
Normalized Value_i = (Deviation_i - Min Deviation) / (Max Deviation - Min Deviation)
2. Absolute Sum Normalization: The deviations are normalized based on the sum of absolute deviations:
Normalized Value_i = Deviation_i / Sum of Absolute Deviations
This normalization ensures that the oscillator values are within a consistent range, facilitating more reliable trend analysis.
For more details:
🔶 Multi-Step Take Profit Mechanism
One of the unique features of this strategy is the multi-step take profit mechanism. This allows traders to lock in profits at multiple levels as the market moves in their favor. The strategy uses three take profit levels, each defined as a percentage increase (for long trades) or decrease (for short trades) from the entry price.
1. First Take Profit Level: Calculated as a percentage increase/decrease from the entry price:
TP_Level1 = Entry Price * (1 + tp_level1 / 100) for long trades
TP_Level1 = Entry Price * (1 - tp_level1 / 100) for short trades
The strategy exits a portion of the position (defined by `tp_percent1`) when this level is reached.
2. Second Take Profit Level: Similar to the first level, but with a higher percentage:
TP_Level2 = Entry Price * (1 + tp_level2 / 100) for long trades
TP_Level2 = Entry Price * (1 - tp_level2 / 100) for short trades
The strategy exits another portion of the position (`tp_percent2`) at this level.
3. Third Take Profit Level: The final take profit level:
TP_Level3 = Entry Price * (1 + tp_level3 / 100) for long trades
TP_Level3 = Entry Price * (1 - tp_level3 / 100) for short trades
The remaining portion of the position (`tp_percent3`) is exited at this level.
This multi-step approach provides a balance between securing profits and allowing the remaining position to benefit from continued favorable market movement.
█ Trade Direction
The strategy allows traders to specify the trade direction through the `tradeDirection` input. The options are:
1. Both: The strategy will take both long and short positions based on the entry signals.
2. Long: The strategy will only take long positions.
3. Short: The strategy will only take short positions.
This flexibility enables traders to tailor the strategy to their market outlook or current trend analysis.
█ Usage
To use the Multi-Step FlexiSuperTrend strategy, traders need to set the input parameters according to their trading style and market conditions. The strategy is designed for versatility, allowing for various market environments, including trending and ranging markets.
Traders can also adjust the multi-step take profit levels and percentages to match their risk management and profit-taking preferences. For example, in highly volatile markets, traders might set wider take profit levels with smaller percentages at each level to capture larger price movements.
The normalization method and the incremental factor can be fine-tuned to adjust the sensitivity of the SuperTrend Polyfactor Oscillator, making the strategy more responsive to minor market shifts or more focused on significant trends.
█ Default Settings
The default settings of the strategy are carefully chosen to provide a balanced approach between risk management and profit potential. Here is a breakdown of the default settings and their effects on performance:
1. Indicator Length (10): This parameter controls the lookback period for the ATR calculation. A shorter length makes the strategy more sensitive to recent price movements, potentially generating more signals. A longer length smooths out the ATR, reducing sensitivity but filtering out noise.
2. Starting Factor (0.618): This is the initial multiplier used in the SuperTrend calculation. A lower starting factor makes the SuperTrend bands closer to the price, generating more frequent trend changes. A higher starting factor places the bands further away, filtering out minor fluctuations.
3. Increment Factor (0.382): This parameter controls how much the factor increases with each iteration of the SuperTrend calculation. A smaller increment factor results in more gradual changes in sensitivity, while a larger increment factor creates a wider range of sensitivity across the iterations.
4. Normalization Method (None): The default is no normalization, meaning the raw deviations are used. Normalization methods like Max-Min or Absolute Sum can make the deviations more consistent across different market conditions, improving the reliability of the oscillator.
5. Take Profit Levels (2%, 8%, 18%): These levels define the thresholds for exiting portions of the position. Lower levels (e.g., 2%) capture smaller profits quickly, while higher levels (e.g., 18%) allow positions to run longer for more significant gains.
6. Take Profit Percentages (30%, 20%, 15%): These percentages determine how much of the position is exited at each take profit level. A higher percentage at the first level locks in more profit early, reducing exposure to market reversals. Lower percentages at higher levels allow for a portion of the position to benefit from extended trends.
Strategic Multi-Step Supertrend - Strategy [presentTrading]The code is mainly developed for me to stimulate the multi-step taking profit function for strategies. The result shows the drawdown can be reduced but at the same time reduced the profit as well. It can be a heuristic for futures leverage traders.
█ Introduction and How it is Different
The "Strategic Multi-Step Supertrend" is a trading strategy designed to leverage the power of multiple steps to optimize trade entries and exits across the Supertrend indicator. Unlike traditional strategies that rely on single entry and exit points, this strategy employs a multi-step approach to take profit, allowing traders to lock in gains incrementally. Additionally, the strategy is adaptable to both long and short trades, providing a comprehensive solution for dynamic market conditions.
This template strategy lies in its dual Supertrend calculation, which enhances the accuracy of trend detection and provides more reliable signals for trade entries and exits. This approach minimizes false signals and increases the overall profitability of trades by ensuring that positions are entered and exited at optimal points.
BTC 6h L/S Performance
█ Strategy, How It Works: Detailed Explanation
The "Strategic Multi-Step Supertrend Trader" strategy utilizes two Supertrend indicators calculated with different parameters to determine the direction and strength of the market trend. This dual approach increases the robustness of the signals, reducing the likelihood of entering trades based on false signals. Here is a detailed breakdown of how the strategy operates:
🔶 Supertrend Indicator Calculation
The Supertrend indicator is a trend-following overlay on the price chart, typically used to identify the direction of the trend. It is calculated using the Average True Range (ATR) to ensure that the indicator adapts to market volatility. The formula for the Supertrend indicator is:
Upper Band = (High + Low) / 2 + (Factor * ATR)
Lower Band = (High + Low) / 2 - (Factor * ATR)
Where:
- High and Low are the highest and lowest prices of the period.
- Factor is a user-defined multiplier.
- ATR is the Average True Range over a specified period.
The Supertrend changes its direction based on the closing price in relation to these bands.
🔶 Entry-Exit Conditions
The strategy enters long positions when both Supertrend indicators signal an uptrend, and short positions when both indicate a downtrend. Specifically:
- Long Condition: Supertrend1 < 0 and Supertrend2 < 0
- Short Condition: Supertrend1 > 0 and Supertrend2 > 0
- Long Exit Condition: Supertrend1 > 0 and Supertrend2 > 0
- Short Exit Condition: Supertrend1 < 0 and Supertrend2 < 0
🔶 Multi-Step Take Profit Mechanism
The strategy features a multi-step take profit mechanism, which allows traders to lock in profits incrementally. This is achieved through four user-configurable take profit levels. For each level, the strategy specifies a percentage increase (for long trades) or decrease (for short trades) in the entry price at which a portion of the position is exited:
- Step 1: Exit a portion of the trade at Entry Price * (1 + Take Profit Percent1 / 100)
- Step 2: Exit a portion of the trade at Entry Price * (1 + Take Profit Percent2 / 100)
- Step 3: Exit a portion of the trade at Entry Price * (1 + Take Profit Percent3 / 100)
- Step 4: Exit a portion of the trade at Entry Price * (1 + Take Profit Percent4 / 100)
This staggered exit strategy helps in locking profits at multiple levels, thereby reducing risk and increasing the likelihood of capturing the maximum possible profit from a trend.
BTC Local
█ Trade Direction
The strategy is highly flexible, allowing users to specify the trade direction. There are three options available:
- Long Only: The strategy will only enter long trades.
- Short Only: The strategy will only enter short trades.
- Both: The strategy will enter both long and short trades based on the Supertrend signals.
This flexibility allows traders to adapt the strategy to various market conditions and their own trading preferences.
█ Usage
1. Add the strategy to your trading platform and apply it to the desired chart.
2. Configure the take profit settings under the "Take Profit Settings" group.
3. Set the trade direction under the "Trade Direction" group.
4. Adjust the Supertrend settings in the "Supertrend Settings" group to fine-tune the indicator calculations.
5. Monitor the chart for entry and exit signals as indicated by the strategy.
█ Default Settings
- Use Take Profit: True
- Take Profit Percentages: Step 1 - 6%, Step 2 - 12%, Step 3 - 18%, Step 4 - 50%
- Take Profit Amounts: Step 1 - 12%, Step 2 - 8%, Step 3 - 4%, Step 4 - 0%
- Number of Take Profit Steps: 3
- Trade Direction: Both
- Supertrend Settings: ATR Length 1 - 10, Factor 1 - 3.0, ATR Length 2 - 11, Factor 2 - 4.0
These settings provide a balanced starting point, which can be customized further based on individual trading preferences and market conditions.
Wall Street Cheat Sheet IndicatorThe Wall Street Cheat Sheet Indicator is a unique tool designed to help traders identify the psychological stages of the market cycle based on the well-known Wall Street Cheat Sheet. This indicator integrates moving averages and RSI to dynamically label market stages, providing clear visual cues on the chart.
Key Features:
Dynamic Stage Identification: The indicator automatically detects and labels market stages such as Disbelief, Hope, Optimism, Belief, Thrill, Euphoria, Complacency, Anxiety, Denial, Panic, Capitulation, Anger, and Depression. These stages are derived from the emotional phases of market participants, helping traders anticipate market movements.
Technical Indicators: The script uses two key technical indicators:
200-day Simple Moving Average (SMA): Helps identify long-term market trends.
50-day Simple Moving Average (SMA): Aids in recognizing medium-term trends.
Relative Strength Index (RSI): Assesses the momentum and potential reversal points based on overbought and oversold conditions.
Clear Visual Labels: The current market stage is displayed directly on the chart, making it easy to spot trends and potential turning points.
Usefulness:
This indicator is not just a simple mashup of existing tools. It uniquely combines the concept of market psychology with practical technical analysis tools (moving averages and RSI). By labeling the psychological stages of the market cycle, it provides traders with a deeper understanding of market sentiment and potential future movements.
How It Works:
Disbelief: Detected when the price is below the 200-day SMA and RSI is in the oversold territory, indicating a potential bottom.
Hope: Triggered when the price crosses above the 50-day SMA, with RSI starting to rise but still below 50, suggesting an early uptrend.
Optimism: Occurs when the price is above the 50-day SMA and RSI is between 50 and 70, indicating a strengthening trend.
Belief: When the price is well above the 50-day SMA and RSI is between 70 and 80, showing strong bullish momentum.
Thrill and Euphoria: Identified when RSI exceeds 80, indicating overbought conditions and potential for a peak.
Complacency to Depression: These stages are identified based on price corrections and drops relative to moving averages and declining RSI values.
Best Practices:
High-Time Frame Focus: This indicator works best on high-time frame charts, specifically the 1-week Bitcoin (BTCUSDT) chart. The longer time frame provides a clearer picture of the overall market cycle and reduces noise.
Trend Confirmation: Use in conjunction with other technical analysis tools such as trendlines, Fibonacci retracement levels, and support/resistance zones for more robust trading strategies.
How to Use:
Add the Indicator: Apply the Wall Street Cheat Sheet Indicator to your TradingView chart.
Analyze Market Stages: Observe the dynamic labels indicating the current stage of the market cycle.
Make Informed Decisions: Use the insights from the indicator to time your entries and exits, aligning your trades with the market sentiment.
This indicator is a valuable tool for traders looking to understand market psychology and make informed trading decisions based on the stages of the market cycle.
Kaufman Adaptive Moving Average (KAMA) Strategy [TradeDots]"The Kaufman Adaptive Moving Average (KAMA) Strategy" is a trend-following system that leverages the adaptive qualities of the Kaufman Adaptive Moving Average (KAMA). This strategy is distinguished by its ability to adjust dynamically to market volatility, enhancing trading accuracy by minimizing the effects of false and delayed signals often associated with the Simple Moving Average (SMA).
HOW IT WORKS
This strategy is centered around use of the Kaufman Adaptive Moving Average (KAMA) indicator, which refines the principles of the Exponential Moving Average (EMA) with a superior smoothing technique.
KAMA distinguishes itself by its responsiveness to changes in market prices through an "Efficiency Ratio (ER)." This ratio is computed by dividing the recent absolute net price change by the cumulative sum of the absolute price changes over a specified period. The resulting ER value ranges between 0 and 1, where 0 indicates high market noise and 1 reflects stronger market momentum.
Using ER, we could get the smoothing constant (SC) for the moving average derived using the following formula:
fastest = 2/(fastma_length + 1)
slowest = 2/(slowma_length + 1)
SC = math.pow((ER * (fastest-slowest) + slowest), 2)
The KAMA line is then calculated by applying the SC to the difference between the current price and the previous KAMA.
APPLICATION
For entering long positions, this strategy initializes when there is a sequence of 10 consecutive rising KAMA lines. Conversely, a sequence of 10 consecutive falling KAMA lines triggers sell orders for long positions. The same logic applies inversely for short positions.
DEFAULT SETUP
Commission: 0.01%
Initial Capital: $10,000
Equity per Trade: 80%
Users are advised to adjust and personalize this trading strategy to better match their individual trading preferences and style.
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
Price Based Z-Trend - Strategy [presentTrading]█ Introduction and How it is Different
Z-score: a statistical measurement of a score's relationship to the mean in a group of scores.
Simple but effective approach.
The "Price Based Z-Trend - Strategy " leverages the Z-score, a statistical measure that gauges the deviation of a price from its moving average, normalized against its standard deviation. This strategy stands out due to its simplicity and effectiveness, particularly in markets where price movements often revert to a mean. Unlike more complex systems that might rely on a multitude of indicators, the Z-Trend strategy focuses on clear, statistically significant price movements, making it ideal for traders who prefer a streamlined, data-driven approach.
BTCUSD 6h LS Performance
█ Strategy, How It Works: Detailed Explanation
🔶 Calculation of the Z-score
"Z-score is a statistical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. If a Z-score is 0, it indicates that the data point's score is identical to the mean score. A Z-score of 1.0 would indicate a value that is one standard deviation from the mean. Z-scores may be positive or negative, with a positive value indicating the score is above the mean and a negative score indicating it is below the mean."
The Z-score is central to this strategy. It is calculated by taking the difference between the current price and the Exponential Moving Average (EMA) of the price over a user-defined length, then dividing this by the standard deviation of the price over the same length:
z = (x - μ) /σ
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🔶 Trading Signals
Trading signals are generated based on the Z-score crossing predefined thresholds:
- Long Entry: When the Z-score crosses above the positive threshold.
- Long Exit: When the Z-score falls below the negative threshold.
- Short Entry: When the Z-score falls below the negative threshold.
- Short Exit: When the Z-score rises above the positive threshold.
█ Trade Direction
The strategy allows users to select their preferred trading direction through an input option.
█ Usage
To use this strategy effectively, traders should first configure the Z-score thresholds according to their risk tolerance and market volatility. It's also crucial to adjust the length for the EMA and standard deviation calculations based on historical performance and the expected "noise" in price data.
The strategy is designed to be flexible, allowing traders to refine settings to better capture profitable opportunities in specific market conditions.
█ Default Settings
- Trade Direction: Both
- Standard Deviation Length: 100
- Average Length: 100
- Threshold for Z-score: 1.0
- Bar Color Indicator: Enabled
These settings offer a balanced starting point but can be customized to suit various trading styles and market environments. The strategy's parameters are designed to be adjusted as traders gain experience and refine their approach based on ongoing market analysis.
Z-score is a must-learn approach for every algorithmic trader.
Crypto Stablecoin Supply - Indicator [presentTrading]█ Introduction and How it is Different
The "Stablecoin Supply - Indicator" differentiates itself by focusing on the aggregate supply of major stablecoins—USDT, USDC, and DAI—rather than traditional price-based metrics. Its premise is that fluctuations in the total supply of these stablecoins can serve as leading indicators for broader market movements, offering traders a unique vantage point to anticipate shifts in market sentiment.
BTCUSD 6h for recent bull market
BTCUSD 8h
█ Strategy, How it Works: Detailed Explanation
🔶 Data Collection
The strategy begins with the collection of the closing supply for USDT, USDC, and DAI stablecoins. This data is fetched using a specified timeframe (**`tfInput`**), allowing for flexibility in analysis periods.
🔶 Supply Calculation
The individual supplies of USDT, USDC, and DAI are then aggregated to determine the total stablecoin supply within the market at any given time. This combined figure serves as the foundation for the subsequent statistical analysis.
🔶 Z-Score Computation
The heart of the indicator's strategy lies in the computation of the Z-Score, which is a statistical measure used to identify how far a data point is from the mean, relative to the standard deviation. The formula for the Z-Score is:
Z = (X - μ) / σ
Where:
- Z is the Z-Score
- X is the current total stablecoin supply (TotalStablecoinClose)
- μ (mu) is the mean of the total stablecoin supply over a specified length (len)
- σ (sigma) is the standard deviation of the total stablecoin supply over the same length
A moving average of the Z-Score (**`zScore_ma`**) is calculated over a short period (defaulted to 3) to smooth out the volatility and provide a clearer signal.
🔶 Signal Interpretation
The Z-Score itself is plotted, with its color indicating its relation to a defined threshold (0.382), serving as a direct visual cue for market sentiment. Zones are also highlighted to show when the Z-Score is within certain extreme ranges, suggesting overbought or oversold conditions.
Bull -> Bear
█ Trade Direction
- **Entry Threshold**: A Z-Score crossing above 0.382 suggests an increase in stablecoin supply relative to its historical average, potentially indicating bullish market sentiment or incoming capital flow into cryptocurrencies.
- **Exit Threshold**: Conversely, a Z-Score dropping below -0.382 may signal a reduction in stablecoin supply, hinting at bearish sentiment or capital withdrawal.
█ Usage
Traders can leverage the "Stablecoin Supply - Indicator" to gain insights into the underlying market dynamics that are not immediately apparent through price analysis alone. It is particularly useful for identifying potential shifts in market sentiment before they are reflected in price movements. By integrating this indicator with other technical analysis tools, traders can develop a more rounded and informed trading strategy.
█ Default Settings
- Timeframe Input (`tfInput`): Allows users to specify the timeframe for data collection, adding flexibility to the analysis.
- Z-Score Length (`len`): Set to 252 by default, representing the period over which the mean and standard deviation of the stablecoin supply are calculated.
- Color Coding: Uses distinct colors (green for bullish, red for bearish) to indicate the Z-Score's position relative to its thresholds, enhancing visual clarity.
- Extreme Range Fill: Highlights areas between defined high and low Z-Score thresholds with distinct colors to indicate potential overbought or oversold conditions.
By integrating considerations of stablecoin supply into the analytical framework, the "Stablecoin Supply - Indicator" offers a novel perspective on cryptocurrency market dynamics, enabling traders to make more nuanced and informed decisions.
Session Breakout Scalper Trading BotHi Traders !
Introduction:
I have recently been exploring the world of automated algorithmic trading (as I prefer more objective trading strategies over subjective technical analysis (TA)) and would like to share one of my automation compatible (PineConnecter compatible) scripts “Session Breakout Scalper”.
The strategy is really simple and is based on time conditional breakouts although has more ”relatively” advanced optional features such as the regime indicators (Regime Filters) that attempt to filter out noise by adding more confluence states and the ATR multiple SL that takes into account volatility to mitigate the down side risk of the trade.
What is Algorthmic Trading:
Firstly what is algorithmic trading? Algorithmic trading also known as algo-trading, is a method of using computer programs (in this case pine script) to execute trades based on predetermined rules and instructions (this trading strategy). It's like having a robot trader who follows a strict set of commands to buy and sell assets automatically, without any human intervention.
Important Note:
For Algorithmic trading the strategy will require you having an essential TV subscription at the minimum (so that you can set alerts) plus a PineConnecter subscription (scroll down to the .”How does the strategy send signals” headings to read more)
The Strategy Explained:
Is the Time input true ? (this can be changed by toggling times under the “TRADE MEDIAN TIMES” group for user inputs).
Given the above is true the strategy waits x bars after the session and then calculates the highest high (HH) to lowest low (LL) range. For this box to form, the user defined amount of bars must print after the session. The box is symmetrical meaning the HH and LL are calculated over a lookback that is equal to the sum of user defined bars before and after the session (+ 1).
The Strategy then simultaneously defines the HH as the buy level (green line) and the LL as the sell level (red line). note the strategy will set stop orders at these levels respectively.
Enter a buy if price action crosses above the HH, and then cancel the sell order type (The opposite is true for a stop order).
If the momentum based regime filters are true the strategy will check for the regime / regimes to be true, if the regime if false the strategy will exit the current trade, as the regime filter has predicted a slowing / reversal of momentum.
The image below shows the strategy executing these trading rules ( Regime filters, "Trades on chart", "Signal & Label" and "Quantity" have been omitted. "Strategy label plots" has been switched to true)
Other Strategy Rules:
If a new session (time session which is user defined) is true (blue vertical line) and the strategy is currently still in a trade it will exit that trade immediately.
It is possible to also set a range of percentage gain per day that the strategy will try to acquire, if at any point the strategy’s profit is within the percentage range then the position / trade will be exited immediately (This can be changed in the “PERCENT DAY GAIN” group for user inputs)
Stops and Targets:
The strategy has either static (fixed) or variable SL options. TP however is only static. The “STRAT TP & TP” group of user inputs is responsible for the SL and TP values (quoted in pips). Note once the ATR stop is set to true the SL values in the above group no longer have any affect on the SL as expected.
What are the Regime Filters:
The Larry Williams Large Trade Index (LWLTI): The Larry Williams Large Trade Index (LWTI) is a momentum-based technical indicator developed by iconic trader Larry Williams. It identifies potential entries and exits for trades by gauging market sentiment, particularly the buying and selling pressure from large market players. Here's a breakdown of the LWTI:
LWLTI components and their interpretation:
Oscillator: It oscillates between 0 and 100, with 50 acting as the neutral line.
Sentiment Meter: Values above 75 suggest a bearish market dominated by large selling, while readings below 25 indicate a bullish market with strong buying from large players.
Trend Confirmation: Crossing above 75 during an uptrend and below 25 during a downtrend confirms the trend's continuation.
The Andean Oscillator (AO) : The Andean Oscillator is a trend and momentum based indicator designed to measure the degree of variations within individual uptrends and downtrends in the prices.
Regime Filter States:
In trading, a regime filter is a tool used to identify the current state or "regime" of the market.
These Regime filters are integrated within the trading strategy to attempt to lower risk (equity volatility and/or draw down). The regime filters have different states for each market order type (buy and sell). When the regime filters are set to true, if these regime states fail to be true the trade is exited immediately.
For Buy Trades:
LWLTI positive momentum state: Quotient of the lagged trailing difference and the ATR > 50
AO positive momentum state: Bull line > Bear line (signal line is omitted)
For Sell Trades:
LWLTI negative momentum stat: Quotient of the lagged trailing difference and the ATR < 50
AO negative momentum state: Bull line < Bear line (signal line is omitted)
How does the Strategy Send Signals:
The strategy triggers a TV alert (you will neet to set a alert first), TV then sends a HTTP request to the automation software (PineConnecter) which receives the request and then communicates to an MT4/5 EA to automate the trading strategy.
For the strategy to send signals you must have the following
At least a TV essential subscription
This Script added to your chart
A PineConnecter account, which is paid and not free. This will provide you with the expert advisor that executes trades based on these strategies signals.
For more detailed information on the automation process I would recommend you read the PineConnecter documentation and FAQ page.
Dashboard:
This Dashboard (top right by defualt) lists some simple trading statistics and also shows when a trade is live.
Important Notice:
- USE THIS STRATEGY AT YOUR OWN RISK AND ALWAYS DO YOUR OWN RESEARCH & MANUAL BACKTESTING !
- THE STRATEGY WILL NOT EXHIBIT THE BACKTEST PERFORMANCE SEEN BELOW IN ALL MARKETS !
Worm *Public*This Pine Script code is designed to create a custom technical indicator called "Worm" that helps identify trends in the market based on momentum. Let's break down the code and its settings:
Indicator Title and Overlay:
The indicator is named "Worm (Clean)" and is set to be overlaid on the price chart.
Input Settings:
The code defines various input settings, which can be customized by the user. These settings include:
Indicator Settings (e.g., Alpha, Gap)
Backtest Settings (e.g., HighlightCrossovers, ApplyNorm)
Color Settings (e.g., Buy Color, Sell Color, Wait Color)
Location Settings for displaying the indicator above, below, or at the price.
Toggleable Inputs:
These settings allow you to choose whether the momentum indicator should be displayed above, below, or at the price chart. You can also specify the colors for buy, sell, and wait signals.
Indicator Calculations:
The code calculates momentum using various formulas involving the source price data (e.g., open, high, low, close). Momentum values are stored in variables L0, L1, L2, L3, and lrsi.
It also calculates the Color values for the indicator based on certain conditions and user-defined settings.
Bcolor and Scolor are used to determine the color of the plotted indicator based on buy and sell conditions.
Bollinger Bands (BB) and Keltner Channels (KC) Calculation:
The code calculates Bollinger Bands (UpperBB and LowerBB) and Keltner Channels (UpperKC and LowerKC) using the source price data.
It also determines whether the market is in a squeeze (SqzOn) or not (NoSqz) based on the relationship between BB and KC.
Signal Generation:
Buy and sell signals are generated based on various conditions, including momentum values and the squeeze state.
The color of the indicator line is determined based on the buy and sell signals.
LagF Calculation:
The LagF variable is calculated based on certain formulas involving the L0Line, L1Line, L2Line, and L3Line values.
Control Color:
The Color variable is used to control the color of the LagF indicator line based on certain conditions.
Plotting:
The momentum indicator (Val) is plotted on the chart with the specified colors and style.
The LagF indicator (Worm) is also plotted with a dynamic color based on market conditions.
Alerts are triggered when buy or sell signals are generated.
Experimental Section:
This section appears to be left for experimentation and may contain additional code or features.
Overall, this Pine Script code calculates and displays a custom momentum-based indicator called "Worm" on a price chart. It generates buy and sell signals based on momentum and squeeze conditions and allows users to customize various settings, including indicator location and colors. The code is designed for technical analysis and trend identification in financial markets.
Davin's 10/200MA Pullback on SPY Strategy v2.0Strategy:
Using 10 and 200 Simple moving averages, we capitalize on price pullbacks on a general uptrend to scalp 1 - 5% rebounds. 200 MA is used as a general indicator for bullish sentiment, 10 MA is used to identify pullbacks in the short term for buy entries.
An optional bonus: market crash of 20% from 52 days high is regarded as a buy the dip signal.
An optional bonus: can choose to exit on MA crossovers using 200 MA as reference MA (etc. Hard stop on 50 cross 200)
Recommended Ticker: SPY 1D (I have so far tested on SPY and other big indexes only, other stocks appear to be too volatile to use the same short period SMA parameters effectively) + AAPL 4H
How it works:
Buy condition is when:
- Price closes above 200 SMA
- Price closes below 10 SMA
- Price dumps at least 20% (additional bonus contrarian buy the dip option)
Entry is on the next opening market day the day after the buy condition candle was fulfilled.
Sell Condition is when:
- Prices closes below 10 SMA
- Hard stop at 15% drawdown from entry price (adjustable parameter)
- Hard stop at medium term and long term MA crossovers (adjustable parameters)
So far this strategy has been pretty effective for me, feel free to try it out and let me know in the comments how you found :)
Feel free to suggest new strategy ideas for discussion and indicator building
CUT MY LOSSESS - Levereged Stop loss + R / R ratio checker Hello traders!
We have heard many times that keep your losses small and allow your profits to grow. But what happens is that we often make the mistake of doing high-margin trades that we cannot afford to lose. The main reason for this problem, in my opinion, is the rush to open a position and not paying attention to how much acceptable loss in each trade is for us? Is our stop loss point compatible with the loss we are willing to accept?
Many of the losses we incur are not due to our erroneous analysis but to the wrong trading strategy, miscalculation of Stop Loss and failure to calculate the Risk/Reward for each trade. At least for most novice traders, these mistakes happen .
This script does not have complicated logic and is designed only as a help for those who are not interested in working with calculators !! I hope that sometimes that we are very excited to buy, looking at this script can give us a serious flip to avoid risk .
This is a basic script that helps us to intuitively check our stop loss in according to our leverage and to guess the approximate risk/reward of our trade. This script assumes that you always trade with half of your total capital. It is also assumed that you routinely use up to ten percent of your capital for each trade. Therefore, the first variable in this script is the amount of tolerable loss in each trade for you, which is set to 25% by default. So if you follow the previous assumptions, each trade will endanger 2.5% of your capital.
Since not all analyzes are ever accurate, we need to enter into positions that have good Risk/Reward ratio, so that even if half of our analysis fails, we will profitable. Therefore, the second variable in this script is the acceptable Risk/Reward ratio for us, which is set to 1:4 by default.
Also, to check the efficiency of the stop-loss with different trading leverage, I add five leverage by default from 1 to 5 as lines on the side of your stop-loss point.
LeV A (Lowest Leverage-WHITE): 1 by default
LeV B (AQUA): 2 by default
LeV C (YELLOW): 3 by default
LeV D (ORANGE): 4 by default
LeV E (Highest Leverage-RED):5 by default
You can change all these leverages and Acceptable margin loss and R/R ratio according to your needs.
You can also hide the leverage lines you are not dealing with through the script settings .
You will also see lines on the side of your target point to check your risk/reward ,so you can approximate your target according to your trading leverage and the risk/reward you accept. you can also hide these R/R lines from the setting.
Important Note: This script is not designed to give you a stop loss point or take profit point.
To find these points, you must use technical analysis methods , and then use this script to check the coordination of these points with your trading strategy.
Using the script is simple, but I will try to explain it with a few examples.
OnTheMoveWith this plot one is able to compare the different % change in the given time frame. It calculates the sma of a given period (defval = 7) for the close/open.
Strategy would be to choose (trade) from one to other asset in order to get higher rates when pumping or lower when dumping.
The Symbol & exchange has to be specified.
defSymbols = BTC, ETH and LINK
defExchange = BINANCE
Fibonacci-Trading-Indicator_2 (Code-Änderung)Tägliche Gewinne mit dem Fibonacci-Trading Indikator
Die Notierungen bewegen sich in liquiden Märkten in Fibonacci-Verhältnissen. Mit diesem Indikator erhalten Sie für Tagesgeschäfte und für Positionstrades auf Basis einer Woche oder eines Monats Informationen, wo Sie in den Markt einsteigen sollten und was das mindeste erreichbare Kursziel ist. Dieses Kursziel liegt bei 61,8% der gestrigen Handelspanne, der Handelspanne der Vorwoche oder der Handelspanne des Vormonats. Dort realisieren Sie Ihre Gewinne.
Erforderliche Eingaben im Eigenschaftenfenster des Indikators:
• Vorwahl Aufwärtstrend/ Abwärtstrend.
• Zeitrahmen des Kursbalkens für das zu ermittelnde mögliche Hoch/ Tief.
• Handelspanne der vorherigen Periode.
• Aktuell tiefstes Tief des vorgewählten Zeitrahmens, wenn die Notierungen steigen.
• Aktuell höchstes Hoch des vorgewählten Zeitrahmens, wenn die Notierungen fallen.
Für die Erkennung der Bewegungsrichtung kann der Camelback-Indikator eine gute Hilfe sein.
Ausgaben im Chart sind die möglichen Kurslevels, für das mögliche Hoch oder Tief des Kursbalkens im eingestellten Zeitrahmen des Trading-Indikators.
Wichtige Bereiche für das Trading sind folgende Kurslevels:
• Einstiegsbereich 0% - 14,6% oder 0% - 23,6%
• Kursziellevel 61,8%
Kaufen/ verkaufen Sie innerhalb des Einstiegsbereichs zwischen 0% und 23,6%, während der Markt die Bewegungsrichtung vorgibt und sich in Richtung des long-/ oder short-Einstiegspunktes bewegt. Das sind jeweils die Kurslevels bei 14,6% oder 23,6%. Der 61,8%-Kurslevel ist das mindeste erwartbare Kursziel. Wir gehen davon aus, dass der aktuelle Kursbalken mindestens 61,8% der Handelsspanne, der vorherigen Periode erreichen wird. Realisieren Sie deshalb die angelaufenen Gewinne mit 50% der Position, wenn die Notierungen den 61,8% - Level erreicht haben. Mit einem geeigneten Trailing-Stopp lassen Sie sich mit der restlichen Position ausstoppen, riskieren Sie dafür aber nicht mehr als 50 % der angelaufenen Gewinne.
Wählen Sie einen kleinen Zeitrahmen, wenn sich die Notierungen noch im Einstiegsbereich bewegen, um die Richtung des Kursbalkens zu erkennen. Für Positionstrades auf Wochen- oder Monatssicht wird für den Einstieg der Einstiegsbereich 0% - bis 14,6% gewählt. Für Tagesgeschäfte (Zeitrahmen Tag vorgewählt) wird wegen der geringeren Tageshandelspanne der Einstiegsbereich 0% - 23,6% empfohlen.
Mit der Vorwahl Jahr und den entsprechenden Eingaben kann auch das mindeste erwartbare Jahreshoch/ Jahrestief ermittelt werden.
Die Fibonacci-Kurslevels lassen sich ein- und ausblenden. Klicken Sie im Chart auf das Zahnrad für „Chart Einstellungen“. Im Menü „Skalierungen“ kann mit der Vorwahl „Label für Indikatornahmen“ und „Label für letzten Indikatorwert“ die Kurslevels angezeigt werden. Schieben Sie den Chart nach rechts um Unterstützungen und Widerstände an den Kurslevels zu finden.
Bei Eingabefehlern oder fehlenden Eingaben zu einem Zeitrahmen wird der Indikator ausgeblendet.
Achten Sie zur Vermeidung von Verlusten auf ihr Handelsmanagement.
Daily profits with the Fibonacci trading indicator
The quotes move in Fibonacci ratios in liquid markets. With this indicator you receive information for daily trades and position trades based on a week or a month, where you should enter the market and what is the minimum price target that you can achieve. This price target is 61.8% of yesterday's trading range, the trading range of the previous week or the trading range of the previous month.
There you realize your profits.Required entries in the properties window of the indicator:
• Preselection uptrend / downtrend.
• Time frame of the price bar for the possible high / low to be determined.
• previous period trading range.
• Current lowest low of the selected time frame when prices rise.
• Current highest high in the selected time frame when prices fall.
The camelback indicator can be of great help in recognizing the direction of movement.
Outputs in the chart are the possible price levels for the possible high or low of the price bar in the set time frame of the trading indicator.
The following price levels are important areas for trading:
• Entry range 0% - 14.6% or 0% - 23.6%
• Target price level 61.8%
Buy / sell within the entry range between 0% and 23.6% as the market sets the direction of movement and moves towards the long / or short entry point. These are the price levels at 14.6% or 23.6%. The 61.8% price level is the minimum expected price target. We assume that the current bar will reach at least 61.8% of the trading range of the previous period. You should therefore realize the profits you have made with 50% of the position when the prices have reached the 61.8% level. With a suitable trailing stop you can be stopped with the rest of the position, but do not risk more than 50% of the profits.
Choose a small time frame when the quotes are still moving in the entry area to see the direction of the price bar. For position trades on a weekly or monthly perspective, the entry range 0% - to 14.6% is chosen. For day-to-day trades (pre-selected time frame), the entry range 0% - 23.6% is recommended due to the lower daily trading range.With the preselection year and the corresponding entries, the minimum expected annual high / annual low can also be determined.
The Fibonacci price levels can be shown and hidden. In the chart click on the gear wheel for “Chart Settings”. In the “Scaling” menu, the price levels can be displayed with the preselection “Label for indicator names” and “Label for last indicator value”. Slide the chart to the right to find support and resistance at the price levels.
In the event of input errors or missing entries for a time frame, the indicator is hidden.
Pay attention to your trade management to avoid losses.
Simple Price Momentum - How To Create A Simple Trading StrategyThis script was built using a logical approach to trading systems. All the details can be found in a step by step guide below. I hope you enjoy it. I am really glad to be part of this community. Thank you all. I hope you not only succeed on your trading career but also enjoy it.
docs.google.com
