Institutional Activity Index [AlgoAlpha]🌟 Introducing the Institutional Activity Index by AlgoAlpha 🌟
Welcome to a powerful new indicator designed to gauge institutional trading activity! This cutting-edge tool combines volume analysis with price movement to derive a unique index that shines a spotlight on potential institutional moves in the market. 🎯📈
Key Features:
🔍 Normalization Period : Adjust the look-back period for normalization to tailor the sensitivity to your trading strategy.
📊 Moving Average Types : Choose from SMA, HMA, EMA, RMA, WMA, or VWMA to smooth the index and pinpoint trends.
🌈 Color-Coded Trends : Instant visual feedback on index trend direction with customizable up and down colors.
🔔 Alerts : Set alerts for when the index shows increasing activity, decreasing activity, or has reached a peak.
Quick Guide to Using the Institutional Activity Index:
1. 📝 Add the Indicator: Add the indicator to favorites. Adjust the normalization period, MA type, and peak detection settings to match your trading style.
2. 📈 Market Analysis: Similar to volume that reflects the amount of collective trading activity, this index reflects an estimate of the amount of trading activity by institutions. A higher value means that institutions are trading the asset more, this can mean selling or buying as the indicator does not indicate direction . Look out for peak signals, which may indicate that institutions have already secured positions in preparation for a move in price.
3. 🔔 Set Alerts: Enable alerts to notify you when there is a significant change in the activity levels or a new peak is detected, allowing for timely decisions without constant monitoring.
How It Works: 🛠
It is common knowledge that institutions trade with high amounts of capital, but employ tactics so as to not move the price significantly when entering on positions. This can be done by entering in times of high liquidity so that when an institution buys, there are enough sellers to cancel out the price movements and prevent a huge pump in price and vice versa. The Institutional Activity Index calculates liquidity by measuring the volume relative to the price range (close-open). This value is smoothed using median and a user defined moving average type and period, enhancing its clarity. If normalization is enabled, the index is adjusted relative to its range over a user-defined period, making the data comparable across different conditions.
Embrace this innovative tool to enhance your trading insights and strategies! 🚀✨
ความผันผวน
Johnny's Adjusted BB Buy/Sell Signal"Johnny's Adjusted BB Buy/Sell Signal" leverages Bollinger Bands and moving averages to provide dynamic buy and sell signals based on market conditions. This indicator is particularly useful for traders looking to identify strategic entry and exit points based on volatility and trend analysis.
How It Works
Bollinger Bands Setup: The indicator calculates Bollinger Bands using a specified length and multiplier. These bands serve to identify potential overbought (upper band) or oversold (lower band) conditions.
Moving Averages: Two moving averages are calculated — a trend moving average (trendMA) and a long-term moving average (longTermMA) — to gauge the market's direction over different time frames.
Market Phase Determination: The script classifies the market into bullish or bearish phases based on the relationship of the closing price to the long-term moving average.
Strong Buy and Sell Signals: Enhanced signals are generated based on how significantly the price deviates from the Bollinger Bands, coupled with the average candle size over a specified lookback period. The signals are adjusted based on whether the market is bullish or bearish:
In bullish markets, a strong buy signal is triggered if the price significantly drops below the lower Bollinger Band. Conversely, a strong sell signal is activated when the price rises well above the upper band.
In bearish markets, these signals are modified to be more conservative, adjusting the thresholds for triggering strong buy and sell signals.
Features:
Flexibility: Users can adjust the length of the Bollinger Bands and moving averages, as well as the multipliers and factors that determine the strength of buy and sell signals, making it highly customizable to different trading styles and market conditions.
Visual Aids: The script vividly plots the Bollinger Bands and moving averages, and signals are visually represented on the chart, allowing traders to quickly assess trading opportunities:
Regular buy and sell signals are indicated by simple shapes below or above price bars.
Strong buy and sell signals are highlighted with distinctive colors and placed prominently to catch the trader's attention.
Background Coloring: The background color changes based on the market phase, providing an immediate visual cue of the market's overall sentiment.
Usage:
This indicator is ideal for traders who rely on technical analysis to guide their trading decisions. By integrating both Bollinger Bands and moving averages, it provides a multi-faceted view of market trends and volatility, making it suitable for identifying potential reversals and continuation patterns. Traders can use this tool to enhance their understanding of market dynamics and refine their trading strategies accordingly.
Daily Chart ATR & Movement %This Pine Script, titled "Daily ATR & Movement %," is designed for traders looking to gauge volatility and price movements relative to that volatility directly on their trading chart. The script calculates and displays the Average True Range (ATR) over a 14-day period using daily data, alongside the percentage movement of the current price from the previous day's close, scaled by the ATR. These metrics provide a snapshot of daily volatility and the magnitude of price movements within that context, which can be crucial for making informed trading decisions, especially in markets where volatility is a significant factor.
Key Features:
Daily ATR Calculation: Utilizes the ta.atr(14) function to compute the Average True Range on a daily basis, which measures market volatility by decomposing the entire range of asset prices for that day.
Movement Percentage: The script calculates the movement from yesterday’s closing price to today’s current price as a percentage of the daily ATR. This shows how significant today's price change is relative to the typical daily volatility, which helps in understanding whether the price movement is substantial or trivial.
Customizable Label Display: Traders can customize the display through a user input dropdown menu for label size ("small", "normal", "large", "huge") and a slider for vertical offset. This allows for better visibility and customization based on user preference and screen setup.
Dynamic Label Updates: A label is dynamically updated each bar with the latest ATR value and movement percentage. This ongoing update keeps traders informed in real-time without manual recalculations.
How to Use:
Setup: Apply the indicator to any chart.
Customization: Adjust the label size and vertical position to suit your viewing preference using the script’s input options.
Interpretation: Monitor the displayed ATR value and movement percentage to assess volatility and relative price movements. High percentages could indicate significant moves worth trading, while low percentages suggest minor changes.
This script is particularly useful for traders who rely on volatility-based trading strategies, such as breakout trading, where understanding the context of price movements relative to typical market fluctuations can provide a strategic edge.
Consolidation Score ScreenerIn trading, a consolidation range is like a timeout after a big move in the price of a stock or symbol.
It's when the market takes a breather, neither pushing the price up nor down too hard.
Visually, it looks like the price moving sideways on a chart , with highs and lows staying within a certain range.
so this indicator is created to help myself and you decide if its a ranging market and what's the score of that consolidation range
The score ranges between 0 and 10 , where 10 is the max consolidation score , meaning this stock or the symbol is at its highest peak of consolidation .
What would you see using this indicator ?
Symbols circles , inside these circles it will print the consolidation score ..
in the middle of the indicator it will show the range of all the 20 symbols scores .
so it will give you like overall ranging value for your 20 symbols
Settings :
TimeFrame : TimeFrame input to select which time frame you want your indicator to analysis
Range length : The Range that you would want your indicator to take into consideration when doing its analysis .
Features :
20 symbols analysis
Multi timeframe capability
Enjoy .
Enhanced Predictive ModelThe "Enhanced Predictive Model" is a sophisticated TradingView indicator designed for traders looking for advanced predictive insights into market trends. This model leverages smoothed price data through an Exponential Moving Average (EMA) to ensure a more stable trend analysis and mitigate the effects of price volatility.
**Features of the Enhanced Predictive Model:**
- **Linear Regression Analysis**: Calculates a regression line over the smoothed price data to determine the prevailing market trend.
- **Predictive Trend Line**: Projects future market behavior by extending the current trend line based on the linear regression analysis.
- **EMA Smoothing**: Utilizes a dynamic smoothing mechanism to provide a clear view of the trend without the noise typically associated with raw price data.
- **Visual Trend Indicators**: Offers immediate visual cues through bar coloring, which changes based on the trend direction detected by the regression slope. Green indicates an uptrend, while red suggests a downtrend.
**Key Inputs:**
- **Regression Length**: Determines the number of bars used for the regression analysis, allowing customization based on the user's trading strategy.
- **EMA Length**: Sets the smoothing parameter for the EMA, balancing responsiveness and stability.
- **Future Bars Prediction**: Defines how many bars into the future the predictive line should extend, providing foresight into potential price movements.
- **Smoothing Length**: Adjusts the sensitivity of the trend detection, ideal for different market conditions.
This tool is ideal for traders focusing on medium to long-term trends and can be used across various markets, including forex, stocks, and cryptocurrencies. Whether you are a day trader or a long-term investor, the "Enhanced Predictive Model" offers valuable insights to help anticipate market moves and enhance your trading decisions.
**Usage Tips:**
- Best used in markets with moderate volatility for clearer trend identification.
- Combine with volume indicators or oscillators for a comprehensive trading strategy.
**Recommended for:**
- Trend Following
- Market Prediction
- Volatility Assessment
By employing this indicator, traders can not only follow the market trend but also anticipate changes, giving them a strategic edge in their trading activities.
speed of tradesThis indicator calculates the speed of trades, on other platform that is called speed of tape, but they said you need delta and others for the calculation.
Calculation method
This indicator calculates the number of trades per bar and filter it, if they are above a sma it highlights the column to know that could be a bar where there are more trades than usual.
It's based on an example of pinescript v5 user manual where explain the use of varip
HF Bots filter and common uses
know where there are more trades than usual help you to have an idea that could be HF Bots working on that bar, also if you dont belive on that, can also help you to have an idea of momentum or stoping action.
Why is this indicator original?
The speed of trades indicator give you an counter of number of trades and a filter for bars where there are a lot of trades, so searching speed of tape/trades indicator that don't exist on tradingview, this indicator is original.
How to charge data?
By default it doesn't load historical tick data, this indicator only works on realtime bars.
ATR Oscillator with DotsThe ATR Oscillator with Dots utilizes the Average True Range (ATR), a traditional measure that captures the extent of an asset's price movements within a given timeframe. Rather than depicting these values in a continuous line, the ATR Oscillator represents them as discrete dots, colored according to the price movement direction: green for upward movements when the current close is higher than the previous, and red for downward movements when the current close is lower.
In terms of functionality, the key feature of this oscillator is how it visualizes volatility through the spacing of the dots. During periods of high market volatility, the shifts between red and green dots tend to occur more frequently and with greater disparity in their positioning along the oscillator’s axis. This indicates sharp price changes and high trading activity. Conversely, periods of market consolidation are characterized by fewer color changes and a more clustered arrangement of dots, reflecting less price movement and lower volatility.
Traders can leverage the insights from the ATR Oscillator with Dots to better understand the market's behavior. For instance, a tight clustering of dots around the zero line suggests a consolidation phase, where the price is relatively stable and may be preparing for a breakout. On the other hand, widely spaced dots alternating between red and green signify strong price movements, offering opportunities for traders to capitalize on trends or prepare for potential reversals.
Imagine a scenario where a trader is monitoring a currency pair in a fluctuating forex market. An observed increase in the frequency and gap of alternating red and green dots would suggest a rise in volatility, possibly triggered by economic news or events. This could be an optimal time for the trader to seek entry or exit points, aligning their strategy with the increased activity. Conversely, a reduction in the frequency and gap of dot changes could signal an impending consolidation phase, prompting the trader to adopt a more cautious approach or explore range-bound trading strategies.
Therefore, the ATR Oscillator with Dots not only simplifies the interpretation of volatility and price momentum through visual cues but also enriches the trader’s strategy by highlighting periods of high activity and consolidation. This tool can be crucial for making informed decisions, particularly in fast-moving or uncertain market conditions, and can be effectively paired with other indicators to confirm trends and refine trading tactics.
Multi-Timeframe Trend TableThe "Multi-Timeframe Trend Table" indicator is a tool that consolidates a variety of critical trading metrics into a single, easy-to-read table format. This indicator is especially useful for traders who need to analyze multiple timeframes and indicators simultaneously to make informed trading decisions. By displaying a broad spectrum of data including trend information, rangebound status, volatility levels, VWAP (Volume Weighted Average Price), and specific candlestick patterns, the indicator provides a comprehensive overview of market conditions across different timeframes.
Functionality and Components
At its core, the indicator provides real-time insights into market trends by showing whether each timeframe is experiencing an upward, downward, or neutral trend based on simple moving averages. This is complemented by the "Rangebound" status, which indicates whether the price is trading within a defined range, giving insights into market consolidation periods. This can be critical for identifying breakouts or breakdowns from established ranges.
Volatility Measurement
Another key feature of the indicator is the "Volatility" column, which rates the market's volatility on a scale from 1 to 10. This feature uses the Average True Range (ATR) to assess how drastically prices are changing within a given timeframe, providing a numerical value that helps traders understand the intensity of price movements. High volatility levels (scores above 6) are highlighted, which can be crucial for strategies that prefer high volatility.
VWAP and Candlestick Patterns
The indicator also displays the VWAP, which is essential for traders who focus on volume as it shows the average price a security has traded at throughout the day, based on both volume and price. It is especially useful for traders looking to confirm trend directions or catch potential reversals. Additionally, the "Candle" column enhances the indicator's utility by identifying specific candlestick patterns like Doji, Hammer, Inverted Hammer, Bullish Engulfing, and Bearish Engulfing, which are pivotal for pinpointing momentum changes and potential entry or exit points.
Usage Strategy
Traders can utilize this indicator by setting up specific rules based on the information provided. For instance, a possible strategy could involve entering a trade when a Bullish Engulfing pattern appears in a low-volatility environment as indicated by a volatility score under 6, suggesting a potential uptrend start with limited downside risk. Similarly, a trader might consider exiting a position or taking a short position when a Bearish Engulfing pattern is identified during high volatility periods, signaling possible sharp price declines.
Adaptability and Customization
An added advantage is the indicator’s adaptability; traders can customize which columns to display based on their trading preferences and strategies. Whether focusing on trends, volatility, or candlestick patterns, users can configure the table to match their specific needs. This makes it a versatile tool suited for various trading styles and objectives, from day trading to swing trading.
Overall Utility
Overall, the "Multi-Timeframe Trend Table" indicator is an invaluable asset for traders who manage multiple instruments across different timeframes, offering a bird's-eye view of the markets in one concise table. It aids in quick decision-making by providing all necessary data points at a glance, reducing the need to switch between multiple charts and potentially missing critical market movements. By integrating trend analysis with volatility and candlestick patterns, it equips traders with a powerful synthesis of technical analysis tools to enhance their trading strategies and improve market timing.
Stochastic Z-Score Oscillator Strategy [TradeDots]The "Stochastic Z-Score Oscillator Strategy" represents an enhanced approach to the original "Buy Sell Strategy With Z-Score" trading strategy. Our upgraded Stochastic model incorporates an additional Stochastic Oscillator layer on top of the Z-Score statistical metrics, which bolsters the affirmation of potential price reversals.
We also revised our exit strategy to when the Z-Score revert to a level of zero. This amendment gives a much smaller drawdown, resulting in a better win-rate compared to the original version.
HOW DOES IT WORK
The strategy operates by calculating the Z-Score of the closing price for each candlestick. This allows us to evaluate how significantly the current price deviates from its typical volatility level.
The strategy first takes the scope of a rolling window, adjusted to the user's preference. This window is used to compute both the standard deviation and mean value. With these values, the strategic model finalizes the Z-Score. This determination is accomplished by subtracting the mean from the closing price and dividing the resulting value by the standard deviation.
Following this, the Stochastic Oscillator is utilized to affirm the Z-Score overbought and oversold indicators. This indicator operates within a 0 to 100 range, so a base adjustment to match the Z-Score scale is required. Post Stochastic Oscillator calculation, we recalibrate the figure to lie within the -4 to 4 range.
Finally, we compute the average of both the Stochastic Oscillator and Z-Score, signaling overpriced or underpriced conditions when the set threshold of positive or negative is breached.
APPLICATION
Firstly, it is better to identify a stable trading pair for this technique, such as two stocks with considerable correlation. This is to ensure conformance with the statistical model's assumption of a normal Gaussian distribution model. The ideal performance is theoretically situated within a sideways market devoid of skewness.
Following pair selection, the user should refine the span of the rolling window. A broader window smoothens the mean, more accurately capturing long-term market trends, while potentially enhancing volatility. This refinement results in fewer, yet precise trading signals.
Finally, the user must settle on an optimal Z-Score threshold, which essentially dictates the timing for buy/sell actions when the Z-Score exceeds with thresholds. A positive threshold signifies the price veering away from its mean, triggering a sell signal. Conversely, a negative threshold denotes the price falling below its mean, illustrating an underpriced condition that prompts a buy signal.
Within a normal distribution, a Z-Score of 1 records about 68% of occurrences centered at the mean, while a Z-Score of 2 captures approximately 95% of occurrences.
The 'cool down period' is essentially the number of bars that await before the next signal generation. This feature is employed to dodge the occurrence of multiple signals in a short period.
DEFAULT SETUP
The following is the default setup on EURAUD 1h timeframe
Rolling Window: 80
Z-Score Threshold: 2.8
Signal Cool Down Period: 5
Stochastic Length: 14
Stochastic Smooth Period: 7
Commission: 0.01%
Initial Capital: $10,000
Equity per Trade: 40%
FURTHER IMPLICATION
The Stochastic Oscillator imparts minimal impact on the current strategy. As such, it may be beneficial to adjust the weightings between the Z-Score and Stochastic Oscillator values or the scale of Stochastic Oscillator to test different performance outcomes.
Alternative momentum indicators such as Keltner Channels or RSI could also serve as robust confirmations of overbought and oversold signals when used for verification.
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.
Garman-Klass-Yang-Zhang Volatility EstimatorThe Garman-Klass-Yang-Zhang Volatility Estimator (GKYZVE) is yet another attempt to robustly measure volatility, integrating intra-candle and inter-candle dynamics. It is an extension of the Garman-Klass Volatility Estimator (GKVE) incorporating insights from the Yang-Zhang Volatility Estimator (YZVE) . Like the YZVE, the GKYZVE holistically considers open, high, low, and close prices. The formula for GKYZ is:
GKYZVE = 0.5 * σ_HL² + * σ_CC² + σ_OC²
Where:
σ_HL² is the variance based on the high and low prices (σ_HL² = (high - low)² / (4 * math.log(2))), weighted at 0.5.
σ_CC² is the close-to-close variance (σ_CC² = (close - close)²), weighted at (2 ln 2) -1 for the logarithmic distribution of returns and emphasizing the impact of day-to-day price changes.
σ_OC² is the variance of the opening price against the closing price (σ_OC² = 0.5 * (open - close)²), weighted at 1.
The GKYZVE differs from the YZVE by using fixed weighing factors derived from theoretical calculations, leaning heavier into the assumption that returns are log-distributed.
This script also offers a choice for normalization between 0 and 1, turning the estimator into an oscillator for comparing current volatility to recent levels. Horizontal lines at user-defined levels are also available for clearer visualization. Both options are off by default.
References:
Garman, M. B., & Klass, M. J. (1980). On the estimation of security price volatilities from historical data. The Journal of Business, 53(1), 67-78.
Yang, D., & Zhang, Q. (2000). Drift-independent volatility estimation based on high, low, open, and close prices. The Journal of Business, 73(3), 477-492.
Volatility Estimator - YZ & RSThe Yang-Zheng Volatility Estimator (YZVE) integrates both intra-candle and inter-candle dynamics, such as overnight and weekend price changes, offering a more detailed analysis compared to traditional methods. The YZVE is proposed to improve over the standard deviation by accounting for the open, high, low, and close prices of trading periods, instead of only the close prices, and attempts to supplant the Parkinson's Volatility Estimator (PVE) by a also capturing inter-candle dynamics. The YZVE is calculated by this formula:
YZ Volatility Squared σ_YZ² = k * σ_o² + σ_rs² + (1 - k) * σ_c²
where k is a weighting factor that adjusts the emphasis between the overnight and close-to-close components, popularly estimated as:
k = 0.34 / (1.34 + (N+1) / (N-1))
where N is the lookback period. Optionally, users may opt to override this calculation with a specified constant (off by default). Next, the
Overnight Volatility Squared σ_o² = (log(O_t / C_(t-1)))²
measures the volatility associated with overnight price changes, from the previous candle's closing price C_(t-1) to the current candle's opening price O_t. It captures the market's reaction to news and events that occur outside of regular trading hours to reflect risk associated with holding positions over non-trading hours and gaps.
Next, the The Rogers-Satchell Volatility Estimator (RSVE) serves as an intermediary step in the computation of YZVE. It aggregates the logarithmic ratios between high, low, open, and close prices within each trading period, focusing on intra-candle volatility without assuming zero inter-candle drift as commonly implicitly assumed in other volatility models:
Rogers-Satchell Volatility Squared σ_rs² = (log(H_t / C_t) * log(H_t / O_t)) + (log(L_t / C_t) * log(L_t / O_t))
Finally,
Close-to-Close Volatility Squared σ_c² = (log(C_t / C_(t-1)))²
measures the volatility from the close of one candle to the close of the next. It reflects the typical candle volatility, similar to naive standard deviation.
This script also includes an option for users to apply the simpler RS Volatility exclusively, focusing on intraday price movements. Additionally, it offers a choice for normalization between 0 and 1, turning the estimator into an oscillator for comparing current volatility to recent levels. Horizontal lines at user-defined levels are also available for clearer visualization. Both are off by default.
References:
Yang, D., & Zhang, Q. (2000). Drift-independent volatility estimation based on high, low, open, and close prices. The Journal of Business, 73(3), 477-491.
Rogers, L.C.G., & Satchell, S.E. (1991). Estimating variance from high, low and closing prices. Annals of Applied Probability, 1(4), 504-512.
Parkinson's Volatility EstimatorThe Parkinson's Volatility Estimator (PVE) provides an alternative method for assessing market volatility using the highest and lowest prices within a given period. Unlike traditional models that predominantly rely on closing prices, the PVE considers the full range of intra-candle price movements, thereby potentially offering a more comprehensive gauge of market volatility. The estimator is derived from the logarithm of the ratio of the high to low prices, squared and then averaged over the period of interest. This calculation is rooted in the assumption that the logarithmic high-to-low ratio represents a normalized measure of price movements, capturing both upward and downward volatility in a symmetric manner (Parkinson, 1980).
In this specific implementation, the estimator is calculated as follows:
Parkinson’s Volatility = (1/4 log(2)) * (1/n) * Σ from i=1 to n of (log(High_i/Low_i))^2
where n is the lookback period defined by the user, and High_i and Low_i are the highest and lowest prices at each interval i within that period. This formulation takes advantage of the logarithmic properties to scale the volatility measure appropriately, utilizing a factor of 1/4 log(2) to normalize the variance estimate (Parkinson, 1980).
This implementation includes options for output normalization between 0 and 1 and for plotting horizontal lines at specified levels, allowing the estimator to function like an oscillator to evaluate volatility relative to recent market regimes. Users can customize these features through script inputs, enhancing flexibility for various trading scenarios and improving its utility for real-time volatility assessments on the TradingView platform.
Reference:
Parkinson, M. (1980). The extreme value method for estimating the variance of the rate of return. The Journal of Business, 53(1), 61-65.
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
Local
🔶 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.
Sector Rotation Hedging With Volatility Index [TradeDots]The "Sector Rotation Hedging Strategy With Volatility Index" is a comprehensive trading indicator developed to optimally leverage the S&P500 volatility index. It is designed to switch between distinct ETF sectors, strategically hedging to moderate risk exposure during harsh market volatility.
HOW DOES IT WORK
The core of this indicator is grounded on the S&P500 volatility index (VIX) close price and its 60-day moving average. This serves to determine whether the prevailing market volatility is above or below the quarterly average.
In periods of elevated market volatility, risk exposure escalates significantly. Traders retaining stocks in sectors with disproportionately high volatility face increased vulnerability to negative returns. To tackle this, our indicator employs a two-pronged approach utilizing two sequential candlestick close prices to confirm if volatility surpasses the average value.
Upon confirming above-average volatility, a hedging table is deployed to spotlight ETFs with low volatility, such as the Utilities Select Sector SPDR Fund (XLU), to derisk the overall portfolio.
Conversely, in low-volatility conditions, sectors yielding higher returns like the Technology Select Sector SPDR Fund (XLK) are preferred. The hedging table is utilized to earmark high-return sector ETFs.
Thus, during highly volatile market periods, the strategy recommends enhancing portfolio allocation to low-volatility ETFs. During low-volatility windows, the portfolio is calibrated towards high-volatility ETFs for heightened returns.
IMPORTANT CONSIDERATION
In real trading, additional considerations encompassing trading commissions, management fees, and ancillary rotation costs should be factored in. False signals may arise, potentially leading to losses from these fees.
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.
Buy Sell Strategy With Z-Score [TradeDots]The "Buy Sell Strategy With Z-Score" is a trading strategy that harnesses Z-Score statistical metrics to identify potential pricing reversals, for opportunistic buying and selling opportunities.
HOW DOES IT WORK
The strategy operates by calculating the Z-Score of the closing price for each candlestick. This allows us to evaluate how significantly the current price deviates from its typical volatility level.
The strategy first takes the scope of a rolling window, adjusted to the user's preference. This window is used to compute both the standard deviation and mean value. With these values, the strategic model finalizes the Z-Score. This determination is accomplished by subtracting the mean from the closing price and dividing the resulting value by the standard deviation.
This approach provides an estimation of the price's departure from its traditional trajectory, thereby identifying market conditions conducive to an asset being overpriced or underpriced.
APPLICATION
Firstly, it is better to identify a stable trading pair for this technique, such as two stocks with considerable correlation. This is to ensure conformance with the statistical model's assumption of a normal Gaussian distribution model. The ideal performance is theoretically situated within a sideways market devoid of skewness.
Following pair selection, the user should refine the span of the rolling window. A broader window smoothens the mean, more accurately capturing long-term market trends, while potentially enhancing volatility. This refinement results in fewer, yet precise trading signals.
Finally, the user must settle on an optimal Z-Score threshold, which essentially dictates the timing for buy/sell actions when the Z-Score exceeds with thresholds. A positive threshold signifies the price veering away from its mean, triggering a sell signal. Conversely, a negative threshold denotes the price falling below its mean, illustrating an underpriced condition that prompts a buy signal.
Within a normal distribution, a Z-Score of 1 records about 68% of occurrences centered at the mean, while a Z-Score of 2 captures approximately 95% of occurrences.
The 'cool down period' is essentially the number of bars that await before the next signal generation. This feature is employed to dodge the occurrence of multiple signals in a short period.
DEFAULT SETUP
The following is the default setup on EURUSD 1h timeframe
Rolling Window: 80
Z-Score Threshold: 2.8
Signal Cool Down Period: 5
Commission: 0.03%
Initial Capital: $10,000
Equity per Trade: 30%
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.
Rise Sense Capital - RSI MACD Spot Buying IndicatorToday, I'll share a spot buying strategy shared by a member @KR陳 within the DATA Trader Alliance Alpha group. First, you need to prepare two indicators:
今天分享一個DATA交易者聯盟Alpha群組裏面的群友@KR陳分享的現貨買入策略。
首先需要準備兩個指標
RSI Indicator (Relative Strength Index) - RSI is a technical analysis tool based on price movements over a period of time to evaluate the speed and magnitude of price changes. RSI calculates the changes in price over a period to determine whether the recent trend is relatively strong (bullish) or weak (bearish).
RSI指標，(英文全名:Relative Strength Index)，中文稱為「相對強弱指標」，是一種以股價漲跌為基礎，在一段時間內的收盤價，用於評估價格變動的速度 (快慢) 與變化 (幅度) 的技術分析工具，RSI藉由計算一段期間內股價的漲跌變化，判斷最近的趨勢屬於偏強 (偏多) 還是偏弱 (偏空)。
MACD Indicator (Moving Average Convergence & Divergence) - MACD is a technical analysis tool proposed by Gerald Appel in the 1970s. It is commonly used in trading to determine trend reversals by analyzing the convergence and divergence of fast and slow lines.
MACD 指標 (Moving Average Convergence & Divergence) 中文名為平滑異同移動平均線指標，MACD 是在 1970 年代由美國人 Gerald Appel 所提出，是一項歷史悠久且經常在交易中被使用的技術分析工具，原理是利用快慢線的交錯，藉以判斷股價走勢的轉折。
In MACD analysis, the most commonly used values are 12, 26, and 9, known as MACD (12,26,9). The market often uses the MACD indicator to determine the future direction of assets and to identify entry and exit points.
在 MACD 的技術分析中，最常用的值為 12 天、26 天、9 天，也稱為 MACD (12,26,9)，市場常用 MACD 指標來判斷操作標的的後市走向，確定波段漲幅並找到進、出場點。
Strategy analysis by member KR陳:
策略解析 by群友 KR陳 ：
Condition 1: RSI value in the previous candle is below oversold zone(30).
條件1：RSI 在前一根的數值低於超賣區（30）
buycondition1 = RSI <30
Condition 2: MACD histogram changes from decreasing to increasing.
條件2：MACD柱由遞減轉遞增
buycondition2 = hist >hist and hist <hist
Strategy Effect Display:
策略效果展示：
Slight modification:
稍微修改：
I've added the ATR-MACD, developed earlier, as a filter signal alongside the classic MACD. The appearance of an upward-facing triangle indicates that the ATR MACD histogram also triggers the condition, aiming to serve as a filtering mechanism.
我在經典的macd作爲條件的同時 也加入了之前開發的ATR-MACD作爲過濾信號 出現朝上的三角圖示代表ATR MACD的柱狀圖一樣觸發條件 希望可以以此起到過濾的作用
Asset/Usage Instructions:
使用標的/使用説明
Through backtesting, it's found that it's not suitable for smaller time frames as there's a lot of noise. It's recommended to use it in assets with a long-term bullish view, focusing on time frames of 12 hours or longer such as 12H, 16H, 1D, 1W to find spot buying opportunities.
經過回測發現 并不適用與一些小級別時區 噪音會非常多，建議在一些長期看漲的標的中切入12小時以上的時區如12H，16H, 1D, 1W 中間尋找現貨買入的機會。
A few thoughts:
Overall, it's a very good indicator strategy for spot buying in the physical market. Thanks to member @KR陳 for sharing!
一些小感言 綜合來看是一個針對現貨買入非常好的指標策略，感謝群友@KR陳的分享！
Price Prediction With Rolling Volatility [TradeDots]The "Price Prediction With Rolling Volatility" is a trading indicator that estimates future price ranges based on the volatility of price movements within a user-defined rolling window.
HOW DOES IT WORK
This indicator utilizes 3 types of user-provided data to conduct its calculations: the length of the rolling window, the number of bars projecting into the future, and a maximum of three sets of standard deviations.
Firstly, the rolling window. The algorithm amasses close prices from the number of bars determined by the value in the rolling window, aggregating them into an array. It then calculates their standard deviations in order to forecast the prospective minimum and maximum price values.
Subsequently, a loop is initiated running into the number of bars into the future, as dictated by the second parameter, to calculate the maximum price change in both the positive and negative direction.
The third parameter introduces a series of standard deviation values into the forecasting model, enabling users to dictate the volatility or confidence level of the results. A larger standard deviation correlates with a wider predicted range, thereby enhancing the probability factor.
APPLICATION
The purpose of the indicator is to provide traders with an understanding of the potential future movement of the price, demarcating maximum and minimum expected outcomes. For instance, if an asset demonstrates a substantial spike beyond the forecasted range, there's a significantly high probability of that price being rejected and reversed.
However, this indicator should not be the sole basis for your trading decisions. The range merely reflects the volatility within the rolling window and may overlook significant historical price movements. As with any trading strategies, synergize this with other indicators for a more comprehensive and reliable analysis.
Note: In instances where the number of predicted bars is exceedingly high, the lines may become scattered, presumably due to inherent limitations on the TradingView platform. Consequently, when applying three SD in your indicator, it is advised to limit the predicted bars to fewer than 80.
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.
Fibonacci Trend Reversal StrategyIntroduction
This publication introduces the " Fibonacci Retracement Trend Reversal Strategy, " tailored for traders aiming to leverage shifts in market momentum through advanced trend analysis and risk management techniques. This strategy is designed to pinpoint potential reversal points, optimizing trading opportunities.
Overview
The strategy leverages Fibonacci retracement levels derived from @IMBA_TRADER's lance Algo to identify potential trend reversals. It's further enhanced by a method called " Trend Strength Over Time " (TSOT) (by @federalTacos5392b), which utilizes percentile rankings of price action to measure trend strength. This also has implemented Dynamic SL finder by utilizing @veryfid's ATR Stoploss Finder which works pretty well
Indicators:
Fibonacci Retracement Levels : Identifies critical reversal zones at 23.6%, 50%, and 78.6% levels.
TSOT (Trend Strength Over Time) : Employs percentile rankings across various timeframes to gauge the strength and direction of trends, aiding in the confirmation of Fibonacci-based signals.
ATR (Average True Range) : Implements dynamic stop-loss settings for both long and short positions, enhancing trade security.
Strategy Settings :
- Sensitivity: Set default at 18, adjustable for more frequent or sparse signals based on market volatility.
- ATR Stop Loss Finder: Multiplier set at 3.5, applying the ATR value to determine stop losses dynamically.
- ATR Length: Default set to 14 with RMA smoothing.
- TSOT Settings: Hard-coded to identify percentile ranks, with no user-adjustable inputs due to its intrinsic calculation method.
Trade Direction Options : Configurable to support long, short, or both directions, adaptable to the trader's market assessment.
Entry Conditions :
- Long Entry: Triggered when the price surpasses the mid Fibonacci level (50%) with a bullish TSOT signal.
- Short Entry: Activated when the price falls below the mid Fibonacci level with a bearish TSOT indication.
Exit Conditions :
- Employs ATR-based dynamic stop losses, calibrated according to current market volatility, ensuring effective risk management.
Strategy Execution :
- Risk Management: Features adjustable risk-reward settings and enables partial take profits by default to systematically secure gains.
- Position Reversal: Includes an option to reverse positions based on new TSOT signals, improving the strategy's responsiveness to evolving market conditions.
The strategy is optimized for the BYBIT:WIFUSDT.P market on a scalping (5-minute) timeframe, using the default settings outlined above.
I spent a lot of time creating the dynamic exit strategies for partially taking profits and reversing positions so please make use of those and feel free to adjust the settings, tool tips are also provided.
For Developers: this is published as open-sourced code so that developers can learn something especially on dynamic exits and partial take profits!
Good Luck!
Disclaimer
This strategy is shared for educational purposes and must be thoroughly tested under diverse market conditions. Past performance does not guarantee future results. Traders are advised to integrate this strategy with other analytical tools and tailor it to specific market scenarios. I was only sharing what I've crafted while strategizing over a Solana Meme Coin.
Multi Timeframe ATR IndicatorThe Average True Range (ATR) indicator is a technical analysis tool used to measure market volatility. The ATR indicator is designed to capture the degree of price movement or price volatility over a specified period of time. It does this by calculating the true range for each bar or candlestick on a chart and then taking an average of these true range values over a set period.
In the provided Pine Script code, the ATR indicator is being calculated for two different timeframes, which allows traders to compare volatility across different periods. The script includes user-defined inputs for the length of the ATR calculation and the type of smoothing (RMA or SMA) to be applied to the true range values. The 'smoothingFunc' function within the script determines whether to use the RMA (Relative Moving Average) or SMA (Simple Moving Average) based on the user's selection.
The true range for each bar is calculated as the maximum of the following three values: the difference between the current high and low, the absolute value of the difference between the current high and the previous close, and the absolute value of the difference between the current low and the previous close. This calculation is designed to ensure that gaps and limit moves are properly accounted for in the volatility measurement.
The script then uses the 'smoothingFunc' to calculate the ATR values for the two timeframes, and these values are plotted on the chart as two separate lines, allowing traders to visually assess the volatility levels.
Overall, this custom ATR indicator is a versatile tool for traders who wish to analyse market volatility and compare it across different timeframes, potentially aiding in making more informed trading decisions based on the prevailing market conditions.
VIX and SKEW RSI Moving AveragesSKEW and VIX are both indicators of market volatility and risk, but they represent different aspects.
VIX (CBOE Volatility Index) :.
The VIX is a well-known indicator for predicting future market volatility. It is calculated primarily based on S&P 500 options premiums and indicates the degree of market instability and risk.
Typically, when the VIX is high, market participants view the future as highly uncertain and expect sharp volatility in stock prices. It is generally considered an indicator of market fear.
SKEW Index :.
The SKEW is a measure of how much market participants estimate the risk of future declines in stock prices, calculated by the CBOE (Chicago Board Options Exchange) and derived from the premium on S&P 500 options.
If the SKEW is high, market participants consider the risk of future declines in stock prices to be high. This generally indicates a "fat tail at the base" of the market and suggests that the market perceives it as very risky.
These indicators are used by market participants to indicate their concerns and expectations about future stock price volatility. In general, when the VIX is high and the SKEW is high, the market is considered volatile and risky. Conversely, when the VIX is low and the SKEW is low, the market is considered relatively stable and low risk.
Inverse Relationship between SKEW and VIX
It is often observed that there is an inverse correlation between SKEW and VIX. In general, the relationship is as follows
High VIX and low SKEW: When the VIX is high and the SKEW is low, the market is considered volatile while the risk of future stock price declines is low. This indicates that the market is exposed to sharp volatility, but market participants do not expect a major decline.
Low VIX and High SKEW: A low VIX and high SKEW indicates that the market is relatively stable, while the risk of future declines in stock prices is considered high. This indicates that the market is calm, but market participants are wary of a sharp future decline.
This inverse correlation is believed to be the result of market participants' psychology and expectations affecting the movements of the VIX and SKEW. For example, when the VIX is high, it is evident that the market is volatile, and under such circumstances, people tend to view the risk of a sharp decline in stock prices as low. Conversely, when the VIX is low, the market is considered relatively stable and the risk of future declines is likely to be higher.
SKEWVIX RSIMACROSS
In order to compare the trends of the SKEW and VIX, the 50-period moving average of the Relative Strength Index (RSI) was used for verification. the RSI is an indicator of market overheating or overcooling, and the 50-period moving average can be used to determine the medium- to long-term trend. This analysis reveals how the inverse correlation between the SKEW and the VIX relates to the long-term moving average of the RSI.
how to use
Moving Average Direction
Rising blue for VIXRSI indicates increased uncertainty in the market
Rising red for SKEWRSI indicates optimism and beyond
RSI moving average crossing
When the SKEW is dominant, market participants are considered less concerned about a black swan event (significant unexpected price volatility). This suggests that the market is stable and willing to take risks. On the other hand, when the VIX is dominant, it indicates increased market volatility. Investors are more concerned about market uncertainty and tend to take more conservative positions to avoid risk. The direction of the moving averages and the crossing of the moving averages of the two indicators can give an indication of the state of the market.
SKEW>VIX Optimistic/Goldilocks
VIX>SKEW Uncertainty/turbulence
The market can be judged as follows.
BestRegards
KC-MACD Entry Master @shrilssThe KC-MACD Entry Master is designed to enhance trading strategies by utilizing Keltner Channels and MACD for dynamic market analysis. This indicator excels in visually identifying market conditions with a sophisticated bar coloring system and an informative MACD Traffic Light feature.
Key Features:
- Dynamic Bar Coloring: The core feature of this indicator is its ability to adjust the color of bars based on their positioning relative to the Keltner Channels and the EMA (Exponential Moving Average). It colors bars lime or red when the closing price is within the Keltner Channels but above or below the EMA, respectively. Additionally, it uses a fuchsia color to indicate breakouts when the price extends beyond the Keltner Channels. This visual aid helps traders quickly identify potential buying or selling opportunities based on market volatility and price action.
- MACD Traffic Light: Positioned at the bottom of the chart, this unique feature displays the histogram color of the MACD, set by default to a 3/10/16 configuration—known as the 3-10 Oscillator. This Traffic Light gives traders an at-a-glance view of the underlying momentum and trend shifts, further aiding in decision-making processes.
- MACD-Based Entry Signals: By calculating the fast and slow moving averages specified by the user, the script determines MACD values and their crossover with a smoothed signal line. Entry points are then highlighted with shapes (e.g., "Buy" or "Sell") plotted on the chart when conditions are met, including alignment with the bar colors for enhanced accuracy.
Long Bar Highlighter @shrilssThe Long Bar Highlighter is designed to detect long bars that exhibit significant price expansion beyond recent price levels. It highlights bars that exceed the length of the previous four bars, marking them for their potential importance in market movements. Additionally, the indicator plots directional shapes based on the closing prices, which helps traders visualize potential upward or downward momentum. An optional ATR crossover setting refines these signals, focusing on stronger trends for more optimal trading opportunities.
FOMO Alert (Miu)This indicator won't plot anything to the chart.
Please follow steps below to set your alarms based on price range variation:
1) Add indicator to the chart
2) Go to settings
3) Choose timeframe which will be used to calculate bars
4) Choose how many bars which will be used to calculate max and min range
5) Choose max and min range variation (%) to trigger alerts
5) Choose up to 6 different symbols to get alert notification
6) Once all is set go back to the chart and click on 3 dots to set alert in this indicator, rename your alert and confirm
7) You can remove indicator after alert is set and it'll keep working as expected
What does this indicator do?
This indicator will generate alerts based on following conditions:
- If min and max prices reach the range (%) from amount of bars on timeframe set for any symbol checked it will trigger an alert.
- If next set of bars reaches higher range than before it will trigger an alert with new data
- If next set of bars doesn't reach higher range than before it will not trigger alerts, even if they are above the range set (this is to prevent the alert to keep triggering with high frequency)
Once condition is met it will send an alert with the following information:
- Symbol name (e.g: BTC, ETH, LTC)
- Range achieved (e.g: 3,03%)
- Current symbol price and current bar direction (e.g: 63,477.1 ▲)
This script will request lowest and highest prices through request.security() built-in function from all different symbols within the range set. It also requests symbols' price (close) and amount of digits (mintick) for each symbol to send alerts with correct value.
This script was developed with main purpose to send alerts when there are strong price movements and I decided to share with community so anyone can set different parameters for different purposes.
Feel free to give feedbacks on comments section below.
Enjoy!
