Z-Score RSI StrategyOverview
The Z-Score RSI Indicator is an experimental take on momentum analysis. By applying the Relative Strength Index (RSI) to a Z-score of price data, it measures how far prices deviate from their mean, scaled by standard deviation. This isn’t your traditional use of RSI, which is typically based on price data alone. Nevertheless, this unconventional approach can yield unique insights into market trends and potential reversals.
Theory and Interpretation
The RSI calculates the balance between average gains and losses over a set period, outputting values from 0 to 100. Typically, people look at the overbought or oversold levels to identify momentum extremes that might be likely to lead to a reversal. However, I’ve often found that RSI can be effective for trend-following when observing the crossover of its moving average with the midline or the crossover of the RSI with its own moving average. These crossovers can provide useful trend signals in various market conditions.
By combining RSI with a Z-score of price, this indicator estimates the relative strength of the price’s distance from its mean. Positive Z-score trends may signal a potential for higher-than-average prices in the near future (scaled by the standard deviation), while negative trends suggest the opposite. Essentially, when the Z-Score RSI indicates a trend, it reflects that the Z-score (the distance between the average and current price) is likely to continue moving in the trend’s direction. Generally, this signals a potential price movement, though it’s important to note that this could also occur if there’s a shift in the mean or standard deviation, rather than a meaningful change in price itself.
While the Z-Score RSI could be an insightful addition to a comprehensive trading system, it should be interpreted carefully. Mean shifts may validate the indicator’s predictions without necessarily indicating any notable price change, meaning it’s best used in tandem with other indicators or strategies.
Recommendations
Before putting this indicator to use, conduct thorough backtesting and avoid overfitting. The added parameters allow fine-tuning to fit various assets, but be careful not to optimize purely for the highest historical returns. Doing so may create an overly tailored strategy that performs well in backtests but fails in live markets. Keep it balanced and look for robust performance across multiple scenarios, as overfitting is likely to lead to disappointing real-world results.
Cerca negli script per "backtest"
DSL Strategy [DailyPanda]
Overview
The DSL Strategy by DailyPanda is a trading strategy that synergistically combines the idea from indicators to create a more robust and reliable trading tool. By integrating these indicators, the strategy enhances signal accuracy and provides traders with a comprehensive view of market trends and momentum shifts. This combination allows for better entry and exit points, improved risk management, and adaptability to various market conditions.
Combining ideas from indicators adds value by:
Enhancing Signal Confirmation : The strategy requires alignment between trend and momentum before generating trade signals, reducing false entries.
Improving Accuracy : By integrating price action with momentum analysis, the strategy captures more reliable trading opportunities.
Providing Comprehensive Market Insight : The combination offers a better perspective on the market, considering both the direction (trend) and the strength (momentum) of price movements.
How the Components Work Together
1. Trend Identification with DSL Indicator
Dynamic Signal Lines : Calculates upper and lower DSL lines based on a moving average (SMA) and dynamic thresholds derived from recent highs and lows with a specified offset. These lines adapt to market conditions, providing real-time trend insights.
ATR-Based Bands : Adds bands around the DSL lines using the Average True Range (ATR) multiplied by a width factor. These bands account for market volatility and help identify potential stop-loss levels.
Trend Confirmation : The relationship between the price, DSL lines, and bands determines the current trend. For example, if the price consistently stays above the upper DSL line, it indicates a bullish trend.
2. Momentum Analysis
RSI Calculation : Computes the RSI over a specified period to measure the speed and change of price movements.
Zero-Lag EMA (ZLEMA) : Applies a ZLEMA to the RSI to minimize lag and produce a more responsive oscillator.
DSL Application on Oscillator : Implements the DSL concept on the oscillator by calculating dynamic upper and lower levels. This helps identify overbought or oversold conditions more accurately.
Signal Generation : Detects crossovers between the oscillator and its DSL lines. A crossover above the lower DSL line signals potential bullish momentum, while a crossover below the upper DSL line signals potential bearish momentum.
3. Integrated Signal Filtering
Confluence Requirement : A trade signal is generated only when both the DSL indicator and oscillator agree. For instance, a long entry requires both an uptrend confirmation from the DSL indicator and a bullish momentum signal from the oscillator.
Risk Management Integration : The strategy uses the DSL indicator's bands for setting stop-loss levels and calculates take-profit levels based on a user-defined risk-reward ratio. This ensures that every trade has a predefined risk management plan.
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Originality and Value Added to the Community
Unique Synergy : While both indicators are available individually, this strategy is original in how it combines them to enhance their strengths and mitigate their weaknesses, offering a novel approach not present in existing scripts.
Enhanced Reliability : By requiring confirmation from both trend and momentum indicators, the strategy reduces false signals and increases the likelihood of successful trades.
Versatility : The customizable parameters allow traders to adapt the strategy to different instruments, timeframes, and trading styles, making it a valuable tool for a wide range of trading scenarios.
Educational Contribution : The script demonstrates an effective method of combining indicators for improved trading performance, providing insights that other traders can learn from and apply to their own strategies.
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How to Use the Strategy
Adding the Strategy to Your Chart
Apply the DSL Strategy to your desired trading instrument and timeframe on TradingView.
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Configuring Parameters
DSL Indicator Settings :
Length (len) : Adjusts the sensitivity of the DSL lines (default is 34).
Offset : Determines the look-back period for threshold calculations (default is 30).
Bands Width (width) : Changes the distance of the ATR-based bands from the DSL lines (default is 1).
DSL-BELUGA Oscillator Settings :
Beluga Length (len_beluga) : Sets the period for the RSI calculation in the oscillator (default is 10).
DSL Lines Mode (dsl_mode) : Chooses between "Fast" (more responsive) and "Slow" (smoother) modes for the oscillator's DSL lines.
Risk Management :
Risk Reward (risk_reward) : Defines your desired risk-reward ratio for calculating take-profit levels (default is 1.5).
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Interpreting Signals
Long Entry Conditions :
Trend Confirmation : Price is above the upper DSL line and the upper DSL band (dsl_up1 > dsl_dn).
Price Behavior : The last three candles have both their opens and closes above the upper DSL line.
Momentum Signal : The DSL-BELUGA oscillator crosses above its lower DSL line (up_signal), indicating bullish momentum.
Short Entry Conditions :
Trend Confirmation : Price is below the lower DSL line and the lower DSL band (dsl_dn < dsl_up1).
Price Behavior : The last three candles have both their opens and closes below the lower DSL band.
Momentum Signal : The DSL-BELUGA oscillator crosses below its upper DSL line (dn_signal), indicating bearish momentum.
Exit Conditions :
Stop-Loss : Automatically set at the DSL indicator's band level (upper band for longs, lower band for shorts).
Take-Profit : Calculated based on the risk-reward ratio and the initial risk determined by the stop-loss distance.
Visual Aids
Signal Arrows : Upward green arrows for long entries and downward blue arrows for short entries appear on the chart when conditions are met.
Stop-Loss and Take-Profit Lines : Red and green lines display the calculated stop-loss and take-profit levels for active trades.
Background Highlighting : The chart background subtly changes color to indicate when a signal has been generated.
Backtesting and Optimization
Use TradingView's strategy tester to backtest the strategy over historical data.
Adjust parameters to optimize performance for different instruments or market conditions.
Regularly review backtesting results to ensure the strategy remains effective.
RSI Weighted Trend System I [InvestorUnknown]The RSI Weighted Trend System I is an experimental indicator designed to combine both slow-moving trend indicators for stable trend identification and fast-moving indicators to capture potential major turning points in the market. The novelty of this system lies in the dynamic weighting mechanism, where fast indicators receive weight based on the current Relative Strength Index (RSI) value, thus providing a flexible tool for traders seeking to adapt their strategies to varying market conditions.
Dynamic RSI-Based Weighting System
The core of the indicator is the dynamic weighting of fast indicators based on the value of the RSI. In essence, the higher the absolute value of the RSI (whether positive or negative), the higher the weight assigned to the fast indicators. This enables the system to capture rapid price movements around potential turning points.
Users can choose between a threshold-based or continuous weight system:
Threshold-Based Weighting: Fast indicators are activated only when the absolute RSI value exceeds a user-defined threshold. Below this threshold, fast indicators receive no weight.
Continuous Weighting: By setting the weight threshold to zero, the fast indicators always receive some weight, although this can result in more false signals in ranging markets.
// Calculate weight for Fast Indicators based on RSI (Slow Indicator weight is kept to 1 for simplicity)
f_RSI_Weight_System(series float rsi, simple float weight_thre) =>
float fast_weight = na
float slow_weight = na
if weight_thre > 0
if math.abs(rsi) <= weight_thre
fast_weight := 0
slow_weight := 1
else
fast_weight := 0 + math.sqrt(math.abs(rsi))
slow_weight := 1
else
fast_weight := 0 + math.sqrt(math.abs(rsi))
slow_weight := 1
Slow and Fast Indicators
Slow Indicators are designed to identify stable trends, remaining constant in weight. These include:
DMI (Directional Movement Index) For Loop
CCI (Commodity Channel Index) For Loop
Aroon For Loop
Fast Indicators are more responsive and designed to spot rapid trend shifts:
ZLEMA (Zero-Lag Exponential Moving Average) For Loop
IIRF (Infinite Impulse Response Filter) For Loop
Each of these indicators is calculated using a for-loop method to generate a moving average, which captures the trend of a given length range.
RSI Normalization
To facilitate the weighting system, the RSI is normalized from its usual 0-100 range to a -1 to 1 range. This allows for easy scaling when calculating weights and helps the system adjust to rapidly changing market conditions.
// Normalize RSI (1 to -1)
f_RSI(series float rsi_src, simple int rsi_len, simple string rsi_wb, simple string ma_type, simple int ma_len) =>
output = switch rsi_wb
"RAW RSI" => ta.rsi(rsi_src, rsi_len)
"RSI MA" => ma_type == "EMA" ? (ta.ema(ta.rsi(rsi_src, rsi_len), ma_len)) : (ta.sma(ta.rsi(rsi_src, rsi_len), ma_len))
Signal Calculation
The final trading signal is a weighted average of both the slow and fast indicators, depending on the calculated weights from the RSI. This ensures a balanced approach, where slow indicators maintain overall trend guidance, while fast indicators provide timely entries and exits.
// Calculate Signal (as weighted average)
sig = math.round(((DMI*slow_w) + (CCI*slow_w) + (Aroon*slow_w) + (ZLEMA*fast_w) + (IIRF*fast_w)) / (3*slow_w + 2*fast_w), 2)
Backtest Mode and Performance Metrics
This version of the RSI Weighted Trend System includes a comprehensive backtesting mode, allowing users to evaluate the performance of their selected settings against a Buy & Hold strategy. The backtesting includes:
Equity calculation based on the signals generated by the indicator.
Performance metrics table comparing Buy & Hold strategy metrics with the system’s signals, including: Mean, positive, and negative return percentages, Standard deviations (of all, positive and negative returns), Sharpe Ratio, Sortino Ratio, and Omega Ratio
f_PerformanceMetrics(series float base, int Lookback, simple float startDate, bool Annualize = true) =>
// Initialize variables for positive and negative returns
pos_sum = 0.0
neg_sum = 0.0
pos_count = 0
neg_count = 0
returns_sum = 0.0
returns_squared_sum = 0.0
pos_returns_squared_sum = 0.0
neg_returns_squared_sum = 0.0
// Loop through the past 'Lookback' bars to calculate sums and counts
if (time >= startDate)
for i = 0 to Lookback - 1
r = (base - base ) / base
returns_sum += r
returns_squared_sum += r * r
if r > 0
pos_sum += r
pos_count += 1
pos_returns_squared_sum += r * r
if r < 0
neg_sum += r
neg_count += 1
neg_returns_squared_sum += r * r
float export_array = array.new_float(12)
// Calculate means
mean_all = math.round((returns_sum / Lookback) * 100, 2)
mean_pos = math.round((pos_count != 0 ? pos_sum / pos_count : na) * 100, 2)
mean_neg = math.round((neg_count != 0 ? neg_sum / neg_count : na) * 100, 2)
// Calculate standard deviations
stddev_all = math.round((math.sqrt((returns_squared_sum - (returns_sum * returns_sum) / Lookback) / Lookback)) * 100, 2)
stddev_pos = math.round((pos_count != 0 ? math.sqrt((pos_returns_squared_sum - (pos_sum * pos_sum) / pos_count) / pos_count) : na) * 100, 2)
stddev_neg = math.round((neg_count != 0 ? math.sqrt((neg_returns_squared_sum - (neg_sum * neg_sum) / neg_count) / neg_count) : na) * 100, 2)
// Calculate probabilities
prob_pos = math.round((pos_count / Lookback) * 100, 2)
prob_neg = math.round((neg_count / Lookback) * 100, 2)
prob_neu = math.round(((Lookback - pos_count - neg_count) / Lookback) * 100, 2)
// Calculate ratios
sharpe_ratio = math.round(mean_all / stddev_all * (Annualize ? math.sqrt(Lookback) : 1), 2)
sortino_ratio = math.round(mean_all / stddev_neg * (Annualize ? math.sqrt(Lookback) : 1), 2)
omega_ratio = math.round(pos_sum / math.abs(neg_sum), 2)
// Set values in the array
array.set(export_array, 0, mean_all), array.set(export_array, 1, mean_pos), array.set(export_array, 2, mean_neg),
array.set(export_array, 3, stddev_all), array.set(export_array, 4, stddev_pos), array.set(export_array, 5, stddev_neg),
array.set(export_array, 6, prob_pos), array.set(export_array, 7, prob_neu), array.set(export_array, 8, prob_neg),
array.set(export_array, 9, sharpe_ratio), array.set(export_array, 10, sortino_ratio), array.set(export_array, 11, omega_ratio)
// Export the array
export_array
The metrics help traders assess the effectiveness of their strategy over time and can be used to optimize their settings.
Calibration Mode
A calibration mode is included to assist users in tuning the indicator to their specific needs. In this mode, traders can focus on a specific indicator (e.g., DMI, CCI, Aroon, ZLEMA, IIRF, or RSI) and fine-tune it without interference from other signals.
The calibration plot visualizes the chosen indicator's performance against a zero line, making it easy to see how changes in the indicator’s settings affect its trend detection.
Customization and Default Settings
Important Note: The default settings provided are not optimized for any particular market or asset. They serve as a starting point for experimentation. Traders are encouraged to calibrate the system to suit their own trading strategies and preferences.
The indicator allows deep customization, from selecting which indicators to use, adjusting the lengths of each indicator, smoothing parameters, and the RSI weight system.
Alerts
Traders can set alerts for both long and short signals when the indicator flips, allowing for automated monitoring of potential trading opportunities.
RSI 30-50-70 moving averageDescription:
The RSI 30-50-70 Moving Average indicator plots three distinct moving averages based on different RSI ranges (30%, 50%, and 70%). Each moving average corresponds to different market conditions and provides potential entry and exit signals. Here's how it works:
• RSI_30 Range (25%-35%): The moving average of closing prices when the RSI is between 25% and 35%, representing potential oversold conditions.
• RSI_50 Range (45%-55%): The moving average of closing prices when the RSI is between 45% and 55%, providing a balanced perspective for trend-following strategies.
• RSI_70 Range (65%-75%): The moving average of closing prices when the RSI is between 65% and 75%, representing potential overbought conditions.
This indicator offers flexibility, as users can adjust key parameters such as RSI ranges, periods, and time frames to fine-tune the signals for their trading strategies.
How it Works:
Like traditional moving averages, the RSI 30-50-70 Moving Averages can highlight dynamic levels of support and resistance. They offer additional insight by focusing on specific RSI ranges, providing early signals for trend reversals or continuation. The default settings can be used across various assets but should be optimized via backtesting.
Default Settings:
• RSI_30: 25% to 35% (Oversold Zone, yellow line)
• RSI_50: 45% to 55% (Neutral/Trend Zone, green line)
• RSI_70: 65% to 75% (Overbought Zone, red line)
• RSI Period: 14
Buy Conditions:
• Use the 5- or 15-minute time frame.
• Wait for the price to move below the RSI_30 line, indicating potential oversold conditions.
• Enter a buy order when the price closes above the RSI_30 line, signaling a recovery from the oversold zone.
• For a more conservative approach, use the RSI_50 line as the buy signal to confirm a trend reversal.
• Important: Before entering, ensure that the RSI_30 moving average has flattened or started to level off, signaling that the oversold momentum has slowed.
Sell Conditions:
• Use the 5- or 15-minute time frame.
• Wait for the price to close above the RSI_70 line, indicating potential overbought conditions.
• Enter a sell order when the price closes below the RSI_70 line, signaling a decline from the overbought zone.
• Important: Similar to buying, wait for the RSI_70 moving average to flatten or level off before selling, indicating the overbought conditions are stalling.
Key Features:
1. Dynamic Range Customization: The indicator allows users to modify the RSI ranges and periods, tailoring the moving averages to fit different market conditions or asset classes.
2. Trend-Following and Reversal Signals: The RSI 30-50-70 moving averages provide both reversal and trend-following signals, making it a versatile tool for short-term traders.
3. Visual Representation of Market Strength: By plotting moving averages based on RSI levels, traders can visually interpret the market’s strength and potential turning points.
4. Risk Management: The built-in flexibility allows traders to choose lower-risk entries by adjusting which RSI level (e.g., RSI_30 vs. RSI_50) they rely on for signals.
Practical Use:
Different assets respond uniquely to RSI-based moving averages, so it's recommended to backtest and adjust ranges for specific instruments. For example, volatile assets may require wider RSI ranges, while more stable assets could benefit from tighter ranges.
Checking for Buy conditions:
1st: Wait for current price to go below the RSI_30 (yellow line)
2nd: Wait and observe for bullish divergence
3rd: RSI_30 has flattened indicating potential gain of momentum after a bullish divergence.
4th: Enter a buy order when the price closed above the RSI_30, preferably when a green candle appeared.
ANN Trend PredictionThis trend indicator utilizes an artificial neural network (ANN) to predict the next market reversal within a certain range of previous candles. The larger the range of previous candles you set, the fewer reversals will be predicted, and trends will tend to last longer.
The ANN is trained on the BTCUSD 4-hour chart, so using it on other assets or timeframes may yield suboptimal results. It takes three input values: the closing price, the Stochastic RSI, and a Choppiness Indicator. Based on these inputs, the ANN categorizes the current candle as part of an uptrend, downtrend, or as undefined.
Compared to an EMA-based trend indicator, this ANN identifies reversals several candles earlier. It achieves this by detecting subtle patterns in the input values that typically appear before a market turnaround. These patterns are somewhat specific to that chosen asset and timeframe.
The results are displayed using rows of triangles that indicate the predicted price direction. The price levels of the triangles correspond to the closing price at the last reversal. The area between the triangle row and the price is colored green if the ANN correctly predicted the move, and red if it did not.
This indicator is designed to showcase the capabilities and potential of ANNs, and is not intended for actual trading use. The ANN can be trained on any other input values, assets and timeframes for several predictions tasks.
You can use the Predicted_Trend_Signal of this Indicator in any backtest indicator. In the Backtester just grap the Predicted_Trend_Signal. downtrend = 1, uptrend = -1, undefined = 0
Feel free to write me a comment.
Monthly Breakout StrategyThis Monthly High/Low Breakout Strategy is designed to take long or short positions based on breakouts from the high or low of the previous month. Users can select whether they want to go long at a breakout above the previous month’s high, short at a breakdown below the previous month’s low, or use the reverse logic. Additionally, it includes a month filter, allowing trades to be executed only during user-specified months.
Breakout strategies, particularly those based on monthly highs and lows, aim to capitalize on price momentum. These systems rely on the assumption that once a significant price level is breached (such as the previous month's high or low), the market is likely to continue moving in the same direction due to increased volatility and trend-following behaviors by traders. Studies have demonstrated the potential effectiveness of breakout strategies in financial markets.
Scientific Evidence Supporting Breakout Strategies:
Momentum in Financial Markets:
Research on momentum-based strategies, which include breakout trading, shows that securities breaking key levels of support or resistance tend to continue their price movement in the direction of the breakout. Jegadeesh and Titman (1993) found that stocks with strong performance over a given period tend to continue performing well in subsequent periods, a principle also applied to breakout strategies.
Behavioral Finance:
The psychological factor of herd behavior is one of the driving forces behind breakout strategies. When prices break out of a key level (such as a monthly high), it triggers increased buying or selling pressure as traders join the trend. Barberis, Shleifer, and Vishny (1998) explained how cognitive biases, such as overconfidence and sentiment, can amplify price trends, which breakout strategies attempt to exploit.
Market Efficiency:
While markets are generally efficient, periods of inefficiency can occur, particularly around the breakouts of significant price levels. These inefficiencies often result in temporary price trends, which breakout strategies can exploit before the market corrects itself (Fama, 1970).
Risk Considerations:
Despite the potential for profit, the Monthly Breakout Strategy comes with several risks:
False Breakouts:
One of the most common risks in breakout strategies is the occurrence of false breakouts. These happen when the price temporarily moves above (or below) a key level but quickly reverses direction, causing losses for traders who entered positions too early. This is particularly risky in low-volatility environments.
Market Volatility:
Monthly breakout strategies rely on momentum, which may not be consistent across different market conditions. During periods of low volatility, price breakouts might lack the follow-through required for the strategy to succeed, leading to poor performance.
Whipsaw Risk:
The strategy is vulnerable to whipsaw markets, where prices oscillate around key levels without establishing a clear direction. This can result in frequent entry and exit signals that lead to losses, especially if trading costs are not managed properly.
Overfitting to Past Data:
If the month-selection filter is overly optimized based on historical data, the strategy may suffer from overfitting—performing well in backtests but poorly in real-time trading. This happens when strategies are tailored to past market conditions that may not repeat.
Conclusion:
While monthly breakout strategies can be effective in markets with strong momentum, they are subject to several risks, including false breakouts, volatility dependency, and whipsaw behavior. It is crucial to backtest this strategy thoroughly and ensure it aligns with your risk tolerance before implementing it in live trading.
References:
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.
Barberis, N., Shleifer, A., & Vishny, R. (1998). A Model of Investor Sentiment. Journal of Financial Economics, 49(3), 307-343.
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 25(2), 383-417.
Ichimoku Crosses_RSI_AITIchimoku Crosser_RSI_AIT
Overview
The "Ichimoku Cloud Crosses_AIT" strategy is a technical trading strategy that combines the Ichimoku Cloud components with the Relative Strength Index (RSI) to generate trade signals. This strategy leverages the crossovers of the Tenkan-sen and Kijun-sen lines of the Ichimoku Cloud, along with RSI levels, to identify potential entry and exit points for long and short trades. This guide explains the strategy components, conditions, and how to use it effectively in your trading.
1. Strategy Parameters
User Inputs
Tenkan-sen Period (tenkanLength): Default value is 21. This is the period used to calculate the Tenkan-sen line (conversion line) of the Ichimoku Cloud.
Kijun-sen Period (kijunLength): Default value is 120. This is the period used to calculate the Kijun-sen line (base line) of the Ichimoku Cloud.
Senkou Span B Period (senkouBLength): Default value is 52. This is the period used to calculate the Senkou Span B line (leading span B) of the Ichimoku Cloud.
RSI Period (rsiLength): Default value is 14. This period is used to calculate the Relative Strength Index (RSI).
RSI Long Entry Level (rsiLongLevel): Default value is 60. This level indicates the minimum RSI value for a long entry signal.
RSI Short Entry Level (rsiShortLevel): Default value is 40. This level indicates the maximum RSI value for a short entry signal.
2. Strategy Components
Ichimoku Cloud
Tenkan-sen: A short-term trend indicator calculated as the simple moving average (SMA) of the highest high and the lowest low over the Tenkan-sen period.
Kijun-sen: A medium-term trend indicator calculated as the SMA of the highest high and the lowest low over the Kijun-sen period.
Senkou Span A: Calculated as the average of the Tenkan-sen and Kijun-sen, plotted 26 periods ahead.
Senkou Span B: Calculated as the SMA of the highest high and lowest low over the Senkou Span B period, plotted 26 periods ahead.
Chikou Span: The closing price plotted 26 periods behind.
Relative Strength Index (RSI)
RSI: A momentum oscillator that measures the speed and change of price movements. It ranges from 0 to 100 and is used to identify overbought or oversold conditions.
3. Entry and Exit Conditions
Entry Conditions
Long Entry:
The Tenkan-sen crosses above the Kijun-sen (bullish crossover).
The RSI value is greater than or equal to the rsiLongLevel.
Short Entry:
The Tenkan-sen crosses below the Kijun-sen (bearish crossover).
The RSI value is less than or equal to the rsiShortLevel.
Exit Conditions
Exit Long Position: The Tenkan-sen crosses below the Kijun-sen.
Exit Short Position: The Tenkan-sen crosses above the Kijun-sen.
4. Visual Representation
Tenkan-sen Line: Plotted on the chart. The color changes based on its relation to the Kijun-sen (green if above, red if below) and is displayed with a line width of 2.
Kijun-sen Line: Plotted as a white line with a line width of 1.
Entry Arrows:
Long Entry: Displayed as a yellow triangle below the bar.
Short Entry: Displayed as a fuchsia triangle above the bar.
5. How to Use
Apply the Strategy: Apply the "Ichimoku Cloud Crosses_AIT" strategy to your chart in TradingView.
Configure Parameters: Adjust the strategy parameters (Tenkan-sen, Kijun-sen, Senkou Span B, and RSI settings) according to your trading preferences.
Interpret the Signals:
Long Entry: A yellow triangle appears below the bar when a long entry signal is generated.
Short Entry: A fuchsia triangle appears above the bar when a short entry signal is generated.
Monitor Open Positions: The strategy automatically exits positions based on the defined conditions.
Backtesting and Live Trading: Use the strategy for backtesting and live trading. Adjust risk management settings in the strategy properties as needed.
Conclusion
The "Ichimoku Cloud Crosses_AIT" strategy uses Ichimoku Cloud crossovers and RSI to generate trading signals. This strategy aims to capture market trends and potential reversals, providing a structured way to enter and exit trades. Make sure to backtest and optimize the strategy parameters to suit your trading style and market conditions before using it in a live trading environment.
Larry Connors RSI 3 StrategyThe Larry Connors RSI 3 Strategy is a short-term mean-reversion trading strategy. It combines a moving average filter and a modified version of the Relative Strength Index (RSI) to identify potential buying opportunities in an uptrend. The strategy assumes that a short-term pullback within a long-term uptrend is an opportunity to buy at a discount before the trend resumes.
Components of the Strategy:
200-Day Simple Moving Average (SMA): The price must be above the 200-day SMA, indicating a long-term uptrend.
2-Period RSI: This is a very short-term RSI, used to measure the speed and magnitude of recent price changes. The standard RSI is typically calculated over 14 periods, but Connors uses just 2 periods to capture extreme overbought and oversold conditions.
Three-Day RSI Drop: The RSI must decline for three consecutive days, with the first drop occurring from an RSI reading above 60.
RSI Below 10: After the three-day drop, the RSI must reach a level below 10, indicating a highly oversold condition.
Buy Condition: All the above conditions must be satisfied to trigger a buy order.
Sell Condition: The strategy closes the position when the RSI rises above 70, signaling that the asset is overbought.
Who Was Larry Connors?
Larry Connors is a trader, author, and founder of Connors Research, a firm specializing in quantitative trading research. He is best known for developing strategies that focus on short-term market movements. Connors co-authored several popular books, including "Street Smarts: High Probability Short-Term Trading Strategies" with Linda Raschke, which has become a staple among traders seeking reliable, rule-based strategies. His research often emphasizes simplicity and robust testing, which appeals to both retail and institutional traders.
Scientific Foundations
The Relative Strength Index (RSI), originally developed by J. Welles Wilder in 1978, is a momentum oscillator that measures the speed and change of price movements. It oscillates between 0 and 100 and is typically used to identify overbought or oversold conditions in an asset. However, the use of a 2-period RSI in Connors' strategy is unconventional, as most traders rely on longer periods, such as 14. Connors' research showed that using a shorter period like 2 can better capture short-term reversals, particularly when combined with a longer-term trend filter such as the 200-day SMA.
Connors' strategies, including this one, are built on empirical research using historical data. For example, in a study of over 1,000 signals generated by this strategy, Connors found that it performed consistently well across various markets, especially when trading ETFs and large-cap stocks (Connors & Alvarez, 2009).
Risks and Considerations
While the Larry Connors RSI 3 Strategy is backed by empirical research, it is not without risks:
Mean-Reversion Assumption: The strategy is based on the premise that markets revert to the mean. However, in strong trending markets, the strategy may underperform as prices can remain oversold or overbought for extended periods.
Short-Term Nature: The strategy focuses on very short-term movements, which can result in frequent trading. High trading frequency can lead to increased transaction costs, which may erode profits.
Market Conditions: The strategy performs best in certain market environments, particularly in stable uptrends. In highly volatile or strongly trending markets, the strategy's performance can deteriorate.
Data and Backtesting Limitations: While backtests may show positive results, they rely on historical data and do not account for future market conditions, slippage, or liquidity issues.
Scientific literature suggests that while technical analysis strategies like this can be effective in certain market conditions, they are not foolproof. According to Lo et al. (2000), technical strategies may show patterns that are statistically significant, but these patterns often diminish once they are widely adopted by traders.
References
Connors, L., & Alvarez, C. (2009). Short-Term Trading Strategies That Work. TradingMarkets Publishing Group.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. The Journal of Finance, 55(4), 1705-1770.
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Trend Research
RSI Trend Following StrategyOverview
The RSI Trend Following Strategy utilizes Relative Strength Index (RSI) to enter the trade for the potential trend continuation. It uses Stochastic indicator to check is the price is not in overbought territory and the MACD to measure the current price momentum. Moreover, it uses the 200-period EMA to filter the counter trend trades with the higher probability. The strategy opens only long trades.
Unique Features
Dynamic stop-loss system: Instead of fixed stop-loss level strategy utilizes average true range (ATR) multiplied by user given number subtracted from the position entry price as a dynamic stop loss level.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Two layers trade filtering system: Strategy utilizes MACD and Stochastic indicators measure the current momentum and overbought condition and use 200-period EMA to filter trades against major trend.
Trailing take profit level: After reaching the trailing profit activation level script activates the trailing of long trade using EMA. More information in methodology.
Wide opportunities for strategy optimization: Flexible strategy settings allows users to optimize the strategy entries and exits for chosen trading pair and time frame.
Methodology
The strategy opens long trade when the following price met the conditions:
RSI is above 50 level.
MACD line shall be above the signal line
Both lines of Stochastic shall be not higher than 80 (overbought territory)
Candle’s low shall be above the 200 period EMA
When long trade is executed, strategy set the stop-loss level at the price ATR multiplied by user-given value below the entry price. This level is recalculated on every next candle close, adjusting to the current market volatility.
At the same time strategy set up the trailing stop validation level. When the price crosses the level equals entry price plus ATR multiplied by user-given value script starts to trail the price with trailing EMA(by default = 20 period). If price closes below EMA long trade is closed. When the trailing starts, script prints the label “Trailing Activated”.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 1.75)
ATR Trailing Profit Activation Level (by default = 2.25)
MACD Fast Length (by default = 12, period of averaging fast MACD line)
MACD Fast Length (by default = 26, period of averaging slow MACD line)
MACD Signal Smoothing (by default = 9, period of smoothing MACD signal line)
Oscillator MA Type (by default = EMA, available options: SMA, EMA)
Signal Line MA Type (by default = EMA, available options: SMA, EMA)
RSI Length (by default = 14, period for RSI calculation)
Trailing EMA Length (by default = 20, period for EMA, which shall be broken close the trade after trailing profit activation)
Justification of Methodology
This trading strategy is designed to leverage a combination of technical indicators—Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Stochastic Oscillator, and the 200-period Exponential Moving Average (EMA)—to determine optimal entry points for long trades. Additionally, the strategy uses the Average True Range (ATR) for dynamic risk management to adapt to varying market conditions. Let's look in details for which purpose each indicator is used for and why it is used in this combination.
Relative Strength Index (RSI) is a momentum indicator used in technical analysis to measure the speed and change of price movements in a financial market. It helps traders identify whether an asset is potentially overbought (overvalued) or oversold (undervalued), which can indicate a potential reversal or continuation of the current trend.
How RSI Works? RSI tracks the strength of recent price changes. It compares the average gains and losses over a specific period (usually 14 periods) to assess the momentum of an asset. Average gain is the average of all positive price changes over the chosen period. It reflects how much the price has typically increased during upward movements. Average loss is the average of all negative price changes over the same period. It reflects how much the price has typically decreased during downward movements.
RSI calculates these average gains and losses and compares them to create a value between 0 and 100. If the RSI value is above 70, the asset is generally considered overbought, meaning it might be due for a price correction or reversal downward. Conversely, if the RSI value is below 30, the asset is considered oversold, suggesting it could be poised for an upward reversal or recovery. RSI is a useful tool for traders to determine market conditions and make informed decisions about entering or exiting trades based on the perceived strength or weakness of an asset's price movements.
This strategy uses RSI as a short-term trend approximation. If RSI crosses over 50 it means that there is a high probability of short-term trend change from downtrend to uptrend. Therefore RSI above 50 is our first trend filter to look for a long position.
The MACD (Moving Average Convergence Divergence) is a popular momentum and trend-following indicator used in technical analysis. It helps traders identify changes in the strength, direction, momentum, and duration of a trend in an asset's price.
The MACD consists of three components:
MACD Line: This is the difference between a short-term Exponential Moving Average (EMA) and a long-term EMA, typically calculated as: MACD Line = 12 period EMA − 26 period EMA
Signal Line: This is a 9-period EMA of the MACD Line, which helps to identify buy or sell signals. When the MACD Line crosses above the Signal Line, it can be a bullish signal (suggesting a buy); when it crosses below, it can be a bearish signal (suggesting a sell).
Histogram: The histogram shows the difference between the MACD Line and the Signal Line, visually representing the momentum of the trend. Positive histogram values indicate increasing bullish momentum, while negative values indicate increasing bearish momentum.
This strategy uses MACD as a second short-term trend filter. When MACD line crossed over the signal line there is a high probability that uptrend has been started. Therefore MACD line above signal line is our additional short-term trend filter. In conjunction with RSI it decreases probability of following false trend change signals.
The Stochastic Indicator is a momentum oscillator that compares a security's closing price to its price range over a specific period. It's used to identify overbought and oversold conditions. The indicator ranges from 0 to 100, with readings above 80 indicating overbought conditions and readings below 20 indicating oversold conditions.
It consists of two lines:
%K: The main line, calculated using the formula (CurrentClose−LowestLow)/(HighestHigh−LowestLow)×100 . Highest and lowest price taken for 14 periods.
%D: A smoothed moving average of %K, often used as a signal line.
This strategy uses stochastic to define the overbought conditions. The logic here is the following: we want to avoid long trades in the overbought territory, because when indicator reaches it there is a high probability that the potential move is gonna be restricted.
The 200-period EMA is a widely recognized indicator for identifying the long-term trend direction. The strategy only trades in the direction of this primary trend to increase the probability of successful trades. For instance, when the price is above the 200 EMA, only long trades are considered, aligning with the overarching trend direction.
Therefore, strategy uses combination of RSI and MACD to increase the probability that price now is in short-term uptrend, Stochastic helps to avoid the trades in the overbought (>80) territory. To increase the probability of opening long trades in the direction of a main trend and avoid local bounces we use 200 period EMA.
ATR is used to adjust the strategy risk management to the current market volatility. If volatility is low, we don’t need the large stop loss to understand the there is a high probability that we made a mistake opening the trade. User can setup the settings ATR Stop Loss and ATR Trailing Profit Activation Level to realize his own risk to reward preferences, but the unique feature of a strategy is that after reaching trailing profit activation level strategy is trying to follow the trend until it is likely to be finished instead of using fixed risk management settings. It allows sometimes to be involved in the large movements.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.08.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 30%
Maximum Single Position Loss: -3.94%
Maximum Single Profit: +15.78%
Net Profit: +1359.21 USDT (+13.59%)
Total Trades: 111 (36.04% win rate)
Profit Factor: 1.413
Maximum Accumulated Loss: 625.02 USDT (-5.85%)
Average Profit per Trade: 12.25 USDT (+0.40%)
Average Trade Duration: 40 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 2h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
Bollinger Bands Enhanced StrategyOverview
The common practice of using Bollinger bands is to use it for building mean reversion or squeeze momentum strategies. In the current script Bollinger Bands Enhanced Strategy we are trying to combine the strengths of both strategies types. It utilizes Bollinger Bands indicator to buy the local dip and activates trailing profit system after reaching the user given number of Average True Ranges (ATR). Also it uses 200 period EMA to filter trades only in the direction of a trend. Strategy can execute only long trades.
Unique Features
Trailing Profit System: Strategy uses user given number of ATR to activate trailing take profit. If price has already reached the trailing profit activation level, scrip will close long trade if price closes below Bollinger Bands middle line.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Major Trend Filter: Strategy utilizes 100 period EMA to take trades only in the direction of a trend.
Flexible Risk Management: Users can choose number of ATR as a stop loss (by default = 1.75) for trades. This is flexible approach because ATR is recalculated on every candle, therefore stop-loss readjusted to the current volatility.
Methodology
First of all, script checks if currently price is above the 200-period exponential moving average EMA. EMA is used to establish the current trend. Script will take long trades on if this filtering system showing us the uptrend. Then the strategy executes the long trade if candle’s low below the lower Bollinger band. To calculate the middle Bollinger line, we use the standard 20-period simple moving average (SMA), lower band is calculated by the substruction from middle line the standard deviation multiplied by user given value (by default = 2).
When long trade executed, script places stop-loss at the price level below the entry price by user defined number of ATR (by default = 1.75). This stop-loss level recalculates at every candle while trade is open according to the current candle ATR value. Also strategy set the trailing profit activation level at the price above the position average price by user given number of ATR (by default = 2.25). It is also recalculated every candle according to ATR value. When price hit this level script plotted the triangle with the label “Strong Uptrend” and start trail the price at the middle Bollinger line. It also started to be plotted as a green line.
When price close below this trailing level script closes the long trade and search for the next trade opportunity.
Risk Management
The strategy employs a combined and flexible approach to risk management:
It allows positions to ride the trend as long as the price continues to move favorably, aiming to capture significant price movements. It features a user-defined ATR stop loss parameter to mitigate risks based on individual risk tolerance. By default, this stop-loss is set to a 1.75*ATR drop from the entry point, but it can be adjusted according to the trader's preferences.
There is no fixed take profit, but strategy allows user to define user the ATR trailing profit activation parameter. By default, this stop-loss is set to a 2.25*ATR growth from the entry point, but it can be adjusted according to the trader's preferences.
Justification of Methodology
This strategy leverages Bollinger bangs indicator to open long trades in the local dips. If price reached the lower band there is a high probability of bounce. Here is an issue: during the strong downtrend price can constantly goes down without any significant correction. That’s why we decided to use 200-period EMA as a trend filter to increase the probability of opening long trades during major uptrend only.
Usually, Bollinger Bands indicator is using for mean reversion or breakout strategies. Both of them have the disadvantages. The mean reversion buys the dip, but closes on the return to some mean value. Therefore, it usually misses the major trend moves. The breakout strategies usually have the issue with too high buy price because to have the breakout confirmation price shall break some price level. Therefore, in such strategies traders need to set the large stop-loss, which decreases potential reward to risk ratio.
In this strategy we are trying to combine the best features of both types of strategies. Script utilizes ate ATR to setup the stop-loss and trailing profit activation levels. ATR takes into account the current volatility. Therefore, when we setup stop-loss with the user-given number of ATR we increase the probability to decrease the number of false stop outs. The trailing profit concept is trying to add the beat feature from breakout strategies and increase probability to stay in trade while uptrend is developing. When price hit the trailing profit activation level, script started to trail the price with middle line if Bollinger bands indicator. Only when candle closes below the middle line script closes the long trade.
Backtest Results
Operating window: Date range of backtests is 2020.10.01 - 2024.07.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 30%
Maximum Single Position Loss: -9.78%
Maximum Single Profit: +25.62%
Net Profit: +6778.11 USDT (+67.78%)
Total Trades: 111 (48.65% win rate)
Profit Factor: 2.065
Maximum Accumulated Loss: 853.56 USDT (-6.60%)
Average Profit per Trade: 61.06 USDT (+1.62%)
Average Trade Duration: 76 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 4h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
All Chart Patterns [theEccentricTrader]█ OVERVIEW
This indicator automatically draws and sends alerts for all of the chart patterns in my public library as they occur. The patterns included are as follows:
• Ascending Broadening
• Broadening
• Descending Broadening
• Double Bottom
• Double Top
• Triple Bottom
• Triple Top
• Bearish Elliot Wave
• Bullish Elliot Wave
• Bearish Alternate Flag
• Bullish Alternate Flag
• Bearish Flag
• Bullish Flag
• Bearish Ascending Head and Shoulders
• Bullish Ascending Head and Shoulders
• Bearish Descending Head and Shoulders
• Bullish Descending Head and Shoulders
• Bearish Head and Shoulders
• Bullish Head and Shoulders
• Bearish Pennant
• Bullish Pennant
• Ascending Wedge
• Descending Wedge
• Wedge
█ CONCEPTS
Green and Red Candles
• A green candle is one that closes with a close price equal to or above the price it opened.
• A red candle is one that closes with a close price that is lower than the price it opened.
Swing Highs and Swing Lows
• A swing high is a green candle or series of consecutive green candles followed by a single red candle to complete the swing and form the peak.
• A swing low is a red candle or series of consecutive red candles followed by a single green candle to complete the swing and form the trough.
Peak and Trough Prices
• The peak price of a complete swing high is the high price of either the red candle that completes the swing high or the high price of the preceding green candle, depending on which is higher.
• The trough price of a complete swing low is the low price of either the green candle that completes the swing low or the low price of the preceding red candle, depending on which is lower.
Historic Peaks and Troughs
The current, or most recent, peak and trough occurrences are referred to as occurrence zero. Previous peak and trough occurrences are referred to as historic and ordered numerically from right to left, with the most recent historic peak and trough occurrences being occurrence one.
Upper Trends
• A return line uptrend is formed when the current peak price is higher than the preceding peak price.
• A downtrend is formed when the current peak price is lower than the preceding peak price.
• A double-top is formed when the current peak price is equal to the preceding peak price.
Lower Trends
• An uptrend is formed when the current trough price is higher than the preceding trough price.
• A return line downtrend is formed when the current trough price is lower than the preceding trough price.
• A double-bottom is formed when the current trough price is equal to the preceding trough price.
Range
The range is simply the difference between the current peak and current trough prices, generally expressed in terms of points or pips.
Retracement and Extension Ratios
Retracement and extension ratios are calculated by dividing the current range by the preceding range and multiplying the answer by 100. Retracement ratios are those that are equal to or below 100% of the preceding range and extension ratios are those that are above 100% of the preceding range.
Measurement Tolerances
Tolerance refers to the allowable variation or deviation from a specific value or dimension. It is the range within which a particular measurement is considered to be acceptable or accurate. I have applied this concept in my pattern detection logic and have set default tolerances where applicable, as perfect patterns are, needless to say, very rare.
Chart Patterns
Generally speaking price charts are nothing more than a series of swing highs and swing lows. When demand outweighs supply over a period of time prices swing higher and when supply outweighs demand over a period of time prices swing lower. These swing highs and swing lows can form patterns that offer insight into the prevailing supply and demand dynamics at play at the relevant moment in time.
‘Let us assume… that you the reader, are not a member of that mysterious inner circle known to the boardrooms as “the insiders”… But it is fairly certain that there are not nearly so many “insiders” as amateur trader supposes and… It is even more certain that insiders can be wrong… Any success they have, however, can be accomplished only by buying and selling… hey can do neither without altering the delicate poise of supply and demand that governs prices. Whatever they do is sooner or later reflected on the charts where you… can detect it. Or detect, at least, the way in which the supply-demand equation is being affected… So, you do not need to be an insider to ride with them frequently… prices move in trends. Some of those trends are straight, some are curved; some are brief and some are long and continued… produced in a series of action and reaction waves of great uniformity. Sooner or later, these trends change direction; they may reverse (as from up to down), or they may be interrupted by some sort of sideways movement and then, after a time, proceed again in their former direction… when a price trend is in the process of reversal… a characteristic area or pattern takes shape on the chart, which becomes recognisable as a reversal formation… Needless to say, the first and most important task of the technical chart analyst is to learn to know the important reversal formations and to judge what they may signify in terms of trading opportunities’ (Edwards & Magee, 1948).
This is as true today as it was when Edwards and Magee were writing in the first half of the last Century, study your patterns and make judgements for yourself about what their implications truly are on the markets and timeframes you are interested in trading.
Over the years, traders have come to discover a multitude of chart and candlestick patterns that are supposed to pertain information on future price movements. However, it is never so clear cut in practice and patterns that where once considered to be reversal patterns are now considered to be continuation patterns and vice versa. Bullish patterns can have bearish implications and bearish patterns can have bullish implications. As such, I would highly encourage you to do your own backtesting.
There is no denying that chart patterns exist, but their implications will vary from market to market and timeframe to timeframe. So it is down to you as an individual to study them and make decisions about how they may be used in a strategic sense.
█ INPUTS
• Change pattern and label colours
• Show or hide patterns individually
• Adjust pattern ratios and tolerances
• Set or remove alerts for individual patterns
█ NOTES
I have decided to rename some of my previously published patterns based on the way in which the pattern completes. If the pattern completes on a swing high then the pattern is considered bearish, if the pattern completes on a swing low then it is considered bullish. This may seem confusing but it makes sense when you come to backtesting the patterns and want to use the most recent peak or trough prices as stop losses. Patterns that can complete on both a swing high and swing low are for such reasons treated as neutral, namely all broadening and wedge variations. I trust that it is quite self-evident that double and triple bottom patterns are considered bullish while double and triple top patterns are considered bearish, so I did not feel the need to rename those.
The patterns that have been renamed and what they have been renamed to, are as follows:
• Ascending Elliot Waves to Bearish Elliot Waves
• Descending Elliot Waves to Bullish Elliot Waves
• Ascending Head and Shoulders to Bearish Ascending Head and Shoulders
• Descending Head and Shoulders to Bearish Descending Head and Shoulders
• Head and Shoulders to Bearish Head and Shoulders
• Ascending Inverse Head and Shoulders to Bullish Ascending Head and Shoulders
• Descending Inverse Head and Shoulders to Bullish Descending Head and Shoulders
• Inverse Head and Shoulders to Bullish Head and Shoulders
You can test the patterns with your own strategies manually by applying the indicator to your chart while in bar replay mode and playing through the history. You could also automate this process with PineScript by using the conditions from my swing and pattern libraries as entry conditions in the strategy tester or your own custom made strategy screener.
█ LIMITATIONS
All green and red candle calculations are based on differences between open and close prices, as such I have made no attempt to account for green candles that gap lower and close below the close price of the preceding candle, or red candles that gap higher and close above the close price of the preceding candle. This may cause some unexpected behaviour on some markets and timeframes. I can only recommend using 24-hour markets, if and where possible, as there are far fewer gaps and, generally, more data to work with.
█ SOURCES
Edwards, R., & Magee, J. (1948) Technical Analysis of Stock Trends (10th edn). Reprint, Boca Raton, Florida: Taylor and Francis Group, CRC Press: 2013.
Fractal Breakout Trend Following StrategyOverview
The Fractal Breakout Trend Following Strategy is a trend-following system which utilizes the Willams Fractals and Alligator to execute the long trades on the fractal's breakouts which have a high probability to be the new uptrend phase beginning. This system also uses the normalized Average True Range indicator to filter trades after a large moves, because it's more likely to see the trend continuation after a consolidation period. Strategy can execute only long trades.
Unique Features
Trend and volatility filtering system: Strategy uses Williams Alligator to filter the counter-trend fractals breakouts and normalized Average True Range to avoid the trades after large moves, when volatility is high
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Flexible Risk Management: Users can choose the stop-loss percent (by default = 3%) for trades, but strategy also has the dynamic stop-loss level using down fractals.
Methodology
The strategy places stop order at the last valid fractal breakout level. Validity of this fractal is defined by the Williams Alligator indicator. If at the moment of time when price breaking the last fractal price is higher than Alligator's teeth line (8 period SMA shifted 5 bars in the future) this is a valid breakout. Moreover strategy has the additional volatility filtering system using normalized ATR. It calculates the average normalized ATR for last user-defined number of bars and if this value lower than the user-defined threshold value the long trade is executed.
When trade is opened, script places the stop loss at the price higher of two levels: user defined stop-loss from the position entry price or down fractal validation level. The down fractal is valid with the rule, opposite as the up fractal validation. Price shall break to the downside the last down fractal below the Willians Alligator's teeth line.
Strategy has no fixed take profit. Exit level changes with the down fractal validation level. If price is in strong uptrend trade is going to be active until last down fractal is not valid. Strategy closes trade when price hits the down fractal validation level.
Risk Management
The strategy employs a combined approach to risk management:
It allows positions to ride the trend as long as the price continues to move favorably, aiming to capture significant price movements. It features a user-defined stop-loss parameter to mitigate risks based on individual risk tolerance. By default, this stop-loss is set to a 3% drop from the entry point, but it can be adjusted according to the trader's preferences.
Justification of Methodology
This strategy leverages Williams Fractals to open long trade when price has broken the key resistance level to the upside. This resistance level is the last up fractal and is shall be broken above the Williams Alligator's teeth line to be qualified as the valid breakout according to this strategy. The Alligator filtering increases the probability to avoid the false breakouts against the current trend.
Moreover strategy has an additional filter using Average True Range(ATR) indicator. If average value of ATR for the last user-defined number of bars is lower than user-defined threshold strategy can open the long trade according to open trade condition above. The logic here is following: we want to open trades after period of price consolidation inside the range because before and after a big move price is more likely to be in sideways, but we need a trend move to have a profit.
Another one important feature is how the exit condition is defined. On the one hand, strategy has the user-defined stop-loss (3% below the entry price by default). It's made to give users the opportunity to restrict their losses according to their risk-tolerance. On the other hand, strategy utilizes the dynamic exit level which is defined by down fractal activation. If we assume the breaking up fractal is the beginning of the uptrend, breaking down fractal can be the start of downtrend phase. We don't want to be in long trade if there is a high probability of reversal to the downside. This approach helps to not keep open trade if trend is not developing and hold it if price continues going up.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.05.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 30%
Maximum Single Position Loss: -3.19%
Maximum Single Profit: +24.97%
Net Profit: +3036.90 USDT (+30.37%)
Total Trades: 83 (28.92% win rate)
Profit Factor: 1.953
Maximum Accumulated Loss: 963.98 USDT (-8.29%)
Average Profit per Trade: 36.59 USDT (+1.12%)
Average Trade Duration: 72 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 4h and higher time frames and the BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
CE_ZLSMA_5MIN_CANDLECHART-- Overview
The "CE_ZLSMA_5MIN_CANDLECHART" strategy, developed by DailyPanda, is a comprehensive trading strategy designed for analyzing trading on 5-minute candlestick charts.
It aims to use some indicators calculated from a Hekin Ashi chart, while running it on a normal candlestick chart, making sure that no price distortion affects the strategy results .
It also brings a feature to show, on the candlestick chart, where the entries would take place on the HA chart, to also be able to study the effect that the price distortion would make on your backtest.
-- Credit
The code in this script is based on open-source indicators originally written by veryfid and everget, I've made significant changes and additions to the scripts but all credit for the idea goes to them, I just built on top of it:
-- Key Features
It incorporate already built indicators (ZLSMA) and CandelierExit (CE)
-- Zero Lag Least Squares Moving Average (ZLSMA) - by veryfid
The ZLSMA is used to detect trends with minimal lag, improving the accuracy of entry and exit signals.
It incorporates a double-smoothed linear regression to minimize lag and enhance trend-following capabilities.
Buy signals are generated when the price closes above the ZLSMA together with the CE signal.
It is calculated based on the HA candlestick pattern.
-- Chandelier Exit (CE) - by everget
The Chandelier Exit indicator is used to dynamically manage stop-loss levels based on the Average True Range (ATR).
It ensures that stop-loss levels are adaptive to market volatility, protecting profits and limiting losses.
The ATR period and multiplier can be customized to fit different trading styles and risk tolerances.
It is calculated based on the HA candlestick pattern.
-- Heikin Ashi Candles
The strategy leverages Heikin Ashi candlesticks to be able identify trends more clearly and leverage this to stay on winning trades longer.
Traders can choose to display Heikin Ashi candlesticks and order fills on the chart for better visualization.
-- Risk Management
The strategy includes multiple risk management options to protect traders' capital.
Maximum intraday loss limit based on a percentage of equity.
Maximum stop-loss in points to filter out entries with excessive risk.
Daily profit target to stop trading once the goal is achieved.
Options to use fixed contract sizes or dynamically adjust based on a percentage of equity.
These features help traders manage risk and ensure sustainable trading practices.
Moving Averages
Several moving averages (EMA 9, EMA 20, EMA 50, EMA 72, EMA 200, SMA 200, and SMA 500) are plotted to provide additional context and trend confirmation.
A "Zone of Value" is highlighted between the EMA 200 and SMA 200 to identify potential support and resistance areas.
-- Customizable Inputs
The strategy includes various customizable inputs, allowing traders to tailor it to their specific needs.
Start and stop trading times.
Risk management parameters (e.g., maximum stop-loss, daily drawdown limit, and daily profit target).
Display options for Heikin Ashi candles and moving averages.
ZLSMA length and offset.
-- Usage
-- Setting Up the Strategy
Configure the start year for the strategy and the trading hours using the input fields. The first candle of each day will be filled black for easy identification, while candles that are outside the allowed time range will be filled purple.
Customize the risk management parameters to match your risk tolerance and trading style.
Enable or disable the display of Heikin Ashi candlesticks and moving averages as desired.
-- Interpreting Signals
Buy signals are indicated by a "Buy" label when the Heikin Ashi close price is above the ZLSMA and the Chandelier Exit indicates a long position.
The strategy will automatically enter a long position with a stop-loss level determined the swing low.
Positions are closed when the close price falls below the ZLSMA.
-- Risk Management
The strategy monitors the maximum intraday loss and stops trading if the loss limit is reached.
If enabled, also stops trading once the daily profit target is achieved, helping to lock in gains.
You have the option to filter operations based on a maximum accepted stop-loss level, based on your risk tolerance.
You can also operate with a fixed amount of contracts or dynamically adjust it based on your allowed risk per trade, ensuring optimal protection of capital.
-- Visual Aids
The strategy plots various moving averages to provide additional trend context.
The "Zone of Value" between the EMA 200 and SMA 200 highlights potential support and resistance areas.
Heikin Ashi candlesticks and order fills can be displayed to enhance the difference this strategy would take if you were to backtest it on a Heikin Ashi chart.
-- Table of results
This strategy also breaks down the results on a monthly basis for better understanding of your capital development along the way.
-- Conclusion
The "CE_ZLSMA_5MIN_CANDLECHART" strategy is a tool for intraday traders looking to understand and leaverage the Heikin Ashi chart while still using the normal candle chart. Traders can customize the strategy to fit their specific needs, making it a versatile addition to any trading toolkit.
Smart Money Concept Strategy - Uncle SamThis strategy combines concepts from two popular TradingView scripts:
Smart Money Concepts (SMC) : The strategy identifies key levels in the market (swing highs and lows) and draws trend lines to visualize potential breakouts. It uses volume analysis to gauge the strength of these breakouts.
Smart Money Breakouts : This part of the strategy incorporates the idea of "Smart Money" – institutional traders who often lead market movements. It looks for breakouts of established levels with significant volume, aiming to catch the beginning of new trends.
How the Strategy Works:
Identification of Key Levels: The script identifies swing highs and swing lows based on a user-defined lookback period. These levels are considered significant points where price has reversed in the past.
Drawing Trend Lines: Trend lines are drawn connecting these key levels, creating a visual representation of potential support and resistance zones.
Volume Analysis: The script analyzes the volume during the formation of these levels and during breakouts. Higher volume suggests stronger moves and increases the probability of a successful breakout.
Entry Conditions:
Long Entry: A long entry is triggered when the price breaks above a resistance line with significant volume, and the moving average trend filter (optional) is bullish.
Short Entry: A short entry is triggered when the price breaks below a support line with significant volume, and the moving average trend filter (optional) is bearish.
Exit Conditions:
Stop Loss: Customizable stop loss percentages are implemented to protect against adverse price movements.
Take Profit: Customizable take profit percentages are used to lock in profits.
Credits and Compliance:
This strategy is inspired by the concepts and code from "Smart Money Concepts (SMC) " and "Smart Money Breakouts ." I've adapted and combined elements of both scripts to create this strategy. Full credit is given to the original authors for their valuable contributions to the TradingView community.
To comply with TradingView's House Rules, I've made the following adjustments:
Clearly Stated Inspiration: The description explicitly mentions the original scripts and authors as the inspiration for this strategy.
No Direct Copying: The code has been modified and combined, not directly copied from the original scripts.
Educational Purpose: The primary purpose of this strategy is for learning and backtesting. It's not intended as financial advice.
Important Note:
This strategy is intended for educational and backtesting purposes only. It should not be used for live trading without thorough testing and understanding of the underlying concepts. Past performance is not indicative of future results.
Adaptive Trend Classification: Moving Averages [InvestorUnknown]Adaptive Trend Classification: Moving Averages
Overview
The Adaptive Trend Classification (ATC) Moving Averages indicator is a robust and adaptable investing tool designed to provide dynamic signals based on various types of moving averages and their lengths. This indicator incorporates multiple layers of adaptability to enhance its effectiveness in various market conditions.
Key Features
Adaptability of Moving Average Types and Lengths: The indicator utilizes different types of moving averages (EMA, HMA, WMA, DEMA, LSMA, KAMA) with customizable lengths to adjust to market conditions.
Dynamic Weighting Based on Performance: ] Weights are assigned to each moving average based on the equity they generate, with considerations for a cutout period and decay rate to manage (reduce) the influence of past performances.
Exponential Growth Adjustment: The influence of recent performance is enhanced through an adjustable exponential growth factor, ensuring that more recent data has a greater impact on the signal.
Calibration Mode: Allows users to fine-tune the indicator settings for specific signal periods and backtesting, ensuring optimized performance.
Visualization Options: Multiple customization options for plotting moving averages, color bars, and signal arrows, enhancing the clarity of the visual output.
Alerts: Configurable alert settings to notify users based on specific moving average crossovers or the average signal.
User Inputs
Adaptability Settings
λ (Lambda): Specifies the growth rate for exponential growth calculations.
Decay (%): Determines the rate of depreciation applied to the equity over time.
CutOut Period: Sets the period after which equity calculations start, allowing for a focus on specific time ranges.
Robustness Lengths: Defines the range of robustness for equity calculation with options for Narrow, Medium, or Wide adjustments.
Long/Short Threshold: Sets thresholds for long and short signals.
Calculation Source: The data source used for calculations (e.g., close price).
Moving Averages Settings
Lengths and Weights: Allows customization of lengths and initial weights for each moving average type (EMA, HMA, WMA, DEMA, LSMA, KAMA).
Calibration Mode
Calibration Mode: Enables calibration for fine-tuning inputs.
Calibrate: Specifies which moving average type to calibrate.
Strategy View: Shifts entries and exits by one bar for non-repainting backtesting.
Calculation Logic
Rate of Change (R): Calculates the rate of change in the price.
Set of Moving Averages: Generates multiple moving averages with different lengths for each type.
diflen(length) =>
int L1 = na, int L_1 = na
int L2 = na, int L_2 = na
int L3 = na, int L_3 = na
int L4 = na, int L_4 = na
if robustness == "Narrow"
L1 := length + 1, L_1 := length - 1
L2 := length + 2, L_2 := length - 2
L3 := length + 3, L_3 := length - 3
L4 := length + 4, L_4 := length - 4
else if robustness == "Medium"
L1 := length + 1, L_1 := length - 1
L2 := length + 2, L_2 := length - 2
L3 := length + 4, L_3 := length - 4
L4 := length + 6, L_4 := length - 6
else
L1 := length + 1, L_1 := length - 1
L2 := length + 3, L_2 := length - 3
L3 := length + 5, L_3 := length - 5
L4 := length + 7, L_4 := length - 7
// Function to calculate different types of moving averages
ma_calculation(source, length, ma_type) =>
if ma_type == "EMA"
ta.ema(source, length)
else if ma_type == "HMA"
ta.sma(source, length)
else if ma_type == "WMA"
ta.wma(source, length)
else if ma_type == "DEMA"
ta.dema(source, length)
else if ma_type == "LSMA"
lsma(source,length)
else if ma_type == "KAMA"
kama(source, length)
else
na
// Function to create a set of moving averages with different lengths
SetOfMovingAverages(length, source, ma_type) =>
= diflen(length)
MA = ma_calculation(source, length, ma_type)
MA1 = ma_calculation(source, L1, ma_type)
MA2 = ma_calculation(source, L2, ma_type)
MA3 = ma_calculation(source, L3, ma_type)
MA4 = ma_calculation(source, L4, ma_type)
MA_1 = ma_calculation(source, L_1, ma_type)
MA_2 = ma_calculation(source, L_2, ma_type)
MA_3 = ma_calculation(source, L_3, ma_type)
MA_4 = ma_calculation(source, L_4, ma_type)
Exponential Growth Factor: Computes an exponential growth factor based on the current bar index and growth rate.
// The function `e(L)` calculates an exponential growth factor based on the current bar index and a given growth rate `L`.
e(L) =>
// Calculate the number of bars elapsed.
// If the `bar_index` is 0 (i.e., the very first bar), set `bars` to 1 to avoid division by zero.
bars = bar_index == 0 ? 1 : bar_index
// Define the cuttime time using the `cutout` parameter, which specifies how many bars will be cut out off the time series.
cuttime = time
// Initialize the exponential growth factor `x` to 1.0.
x = 1.0
// Check if `cuttime` is not `na` and the current time is greater than or equal to `cuttime`.
if not na(cuttime) and time >= cuttime
// Use the mathematical constant `e` raised to the power of `L * (bar_index - cutout)`.
// This represents exponential growth over the number of bars since the `cutout`.
x := math.pow(math.e, L * (bar_index - cutout))
x
Equity Calculation: Calculates the equity based on starting equity, signals, and the rate of change, incorporating a natural decay rate.
pine code
// This function calculates the equity based on the starting equity, signals, and rate of change (R).
eq(starting_equity, sig, R) =>
cuttime = time
if not na(cuttime) and time >= cuttime
// Calculate the rate of return `r` by multiplying the rate of change `R` with the exponential growth factor `e(La)`.
r = R * e(La)
// Calculate the depreciation factor `d` as 1 minus the depreciation rate `De`.
d = 1 - De
var float a = 0.0
// If the previous signal `sig ` is positive, set `a` to `r`.
if (sig > 0)
a := r
// If the previous signal `sig ` is negative, set `a` to `-r`.
else if (sig < 0)
a := -r
// Declare the variable `e` to store equity and initialize it to `na`.
var float e = na
// If `e ` (the previous equity value) is not available (first calculation):
if na(e )
e := starting_equity
else
// Update `e` based on the previous equity value, depreciation factor `d`, and adjustment factor `a`.
e := (e * d) * (1 + a)
// Ensure `e` does not drop below 0.25.
if (e < 0.25)
e := 0.25
e
else
na
Signal Generation: Generates signals based on crossovers and computes a weighted signal from multiple moving averages.
Main Calculations
The indicator calculates different moving averages (EMA, HMA, WMA, DEMA, LSMA, KAMA) and their respective signals, applies exponential growth and decay factors to compute equities, and then derives a final signal by averaging weighted signals from all moving averages.
Visualization and Alerts
The final signal, along with additional visual aids like color bars and arrows, is plotted on the chart. Users can also set up alerts based on specific conditions to receive notifications for potential trading opportunities.
Repainting
The indicator does support intra-bar changes of signal but will not repaint once the bar is closed, if you want to get alerts only for signals after bar close, turn on “Strategy View” while setting up the alert.
Conclusion
The Adaptive Trend Classification: Moving Averages Indicator is a sophisticated tool for investors, offering extensive customization and adaptability to changing market conditions. By integrating multiple moving averages and leveraging dynamic weighting based on performance, it aims to provide reliable and timely investing signals.
Momentum Alligator 4h Bitcoin StrategyOverview
The Momentum Alligator 4h Bitcoin Strategy is a trend-following trading system that operates on dual time frames. It utilizes the 1D Williams Alligator indicator to identify the prevailing major price trend and seeks trading opportunities on the 4-hour (4h) time frame when the momentum is turning up. The strategy is designed to close trades if the trend fails to develop or holding position if price continues increasing without any significant correction. Note that this strategy is specifically tailored for the 4-hour time frame.
Unique Features
2-layers market noise filtering system: Trades are only initiated in the direction of the 1D trend, determined by the Williams Alligator indicator. This higher time frame confirmation filters out minor trade signals, focusing on more substantial opportunities. At the same time, strategy has additional filter on 4h time frame with Awesome Oscillator which is showing the current price momentum.
Flexible Risk Management: The strategy exclusively opens long positions, resulting in fewer trades during bear markets. It incorporates a dynamic stop-loss mechanism, which can either follow the jaw line of the 4h Alligator or a user-defined fixed stop-loss. This flexibility helps manage risk and avoid non-trending markets.
Methodology
The strategy initiates a long position when the d-line of Stochastic RSI crosses up it's k-line. It means that there is a high probability that price momentum reversed from down to up. To avoid overtrading in potentially choppy markets, it skips the next two trades following a winning trade, anticipating sideways movement after a significant price surge.
This strategy has two layers trades filtering system: 4h and 1D time frames. The first one is awesome oscillator. It shall be increasing and value has to be higher than it's 5-period SMA. This is an additional confirmation that long trade is opened in the direction of the current momentum. As it was mentioned above, all entry signals are validated against the 1D Williams Alligator indicator. A trade is only opened if the price is above all three lines of the 1D Alligator, ensuring alignment with the major trend.
A trade is closed if the price hits the 4h jaw line of the Alligator or reaches the user-defined stop-loss level.
Risk Management
The strategy employs a combined approach to risk management:
It allows positions to ride the trend as long as the price continues to move favorably, aiming to capture significant price movements. It features a user-defined stop-loss parameter to mitigate risks based on individual risk tolerance. By default, this stop-loss is set to a 2% drop from the entry point, but it can be adjusted according to the trader's preferences.
Justification of Methodology
This strategy leverages Stochastic RSI on 4h time frame to open long trade when momentum started reversing to the upside. On the one hand, Stochastic RSI is one of the most sensitive indicator, which allows to react fast on the potential trend reversal. On the other hand, this indicator can be too sensitive and provide a lot of false trend changing signals. To eliminate this weakness we use two-layers trades filtering system.
The first layer is the 4h Awesome oscillator. This is less sensitive momentum indicator. Usually it starts increasing when price has already passed significant distance from the actual reversal point. The strategy opens long trade only is Awesome oscillator is increasing and above it's 5-period SMA. This approach increases the probability to filter the false signals during the choppy market or if the reversal is false.
The second layer filter is the Williams Alligator indicator on 1D time frame. The 1D Alligator serves as a filter for identifying the primary trend and increases probability to avoid the trades with low potential because trading against major trend usually is more risky. It's much better to catch the trend continuation than local bounce.
Last but not least feature of this strategy is close trades condition. It uses the flexible approach. First of all, user can set up the fixed stop-loss according to his own risk-tolerance, by default this value is 2% of price movement. It restricts the potential loss at the moment when trade has just been opened. Moreover strategy utilizes the 4h Williams Alligator's jaw line to exit the trade. If price fell below it trade is closed. This approach helps to not keep open trade if trend is not developing and hold it if price continues going up.
Backtest Results:
Operating window: Date range of backtests is 2021.01.01 - 2024.05.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 50%
Maximum Single Position Loss: -3.04%
Maximum Single Profit: +29.67%
Net Profit: +6228.01 USDT (+62.28%)
Total Trades: 118 (24.58% win rate)
Profit Factor: 1.71
Maximum Accumulated Loss: 1527.69 USDT (-11.52%)
Average Profit per Trade: 52.78 USDT (+0.89%)
Average Trade Duration: 60 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use:
Add the script to favorites for easy access.
Apply to the 4h timeframe desired chart (optimal performance observed on the BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
Wavelet & Fourier Smoothed Volume zone oscillator (W&)FSVZO Indicator id:
USER;e7a774913c1242c3b1354334a8ea0f3c
(only relevant to those that use API requests)
MEANINGFUL DESCRIPTION:
The Volume Zone oscillator breaks up volume activity into positive and negative categories. It is positive when the current closing price is greater than the prior closing price and negative when it's lower than the prior closing price. The resulting curve plots through relative percentage levels that yield a series of buy and sell signals, depending on level and indicator direction.
The Wavelet & Fourier Smoothed Volume Zone Oscillator (W&)FSVZO is a refined version of the Volume Zone Oscillator, enhanced by the implementation of the Discrete Fourier Transform. Its primary function is to streamline price data and diminish market noise, thus offering a clearer and more precise reflection of price trends.
By combining the Wavalet and Fourier aproximation with Ehler's white noise histogram, users gain a comprehensive perspective on volume-related market conditions.
HOW TO USE THE INDICATOR:
The default period is 2 but can be adjusted after backtesting. (I suggest 5 VZO length and NoiceR max length 8 as-well)
The VZO points to a positive trend when it is rising above the 0% level, and a negative trend when it is falling below the 0% level. 0% level can be adjusted in setting by adjusting VzoDifference. Oscillations rising below 0% level or falling above 0% level result in natural trend.
ORIGINALITY & USFULLNESS:
Personal combination of Fourier and Wavalet aproximation of a price which results in less noise Volume Zone Oscillator.
The Wavelet Transform is a powerful mathematical tool for signal analysis, particularly effective in analyzing signals with varying frequency or non-stationary characteristics. It dissects a signal into wavelets, small waves with varying frequency and limited duration, providing a multi-resolution analysis. This approach captures both frequency and location information, making it especially useful for detecting changes or anomalies in complex signals.
The Discrete Fourier Transform (DFT) is a mathematical technique that transforms discrete data from the time domain into its corresponding representation in the frequency domain. This process involves breaking down a signal into its individual frequency components, thereby exposing the amplitude and phase characteristics inherent in each frequency element.
This indicator utilizes the concept of Ehler's Universal Oscillator and displays a histogram, offering critical insights into the prevailing levels of market noise. The Ehler's Universal Oscillator is grounded in a statistical model that captures the erratic and unpredictable nature of market movements. Through the application of this principle, the histogram aids traders in pinpointing times when market volatility is either rising or subsiding.
DETAILED DESCRIPTION:
My detailed description of the indicator and use cases which I find very valuable.
What is oscillator?
Oscillators are chart indicators that can assist a trader in determining overbought or oversold conditions in ranging (non-trending) markets.
What is volume zone oscillator?
Price Zone Oscillator measures if the most recent closing price is above or below the preceding closing price.
Volume Zone Oscillator is Volume multiplied by the 1 or -1 depending on the difference of the preceding 2 close prices and smoothed with Exponential moving Average.
What does this mean?
If the VZO is above 0 and VZO is rising. We have a bullish trend. Most likely.
If the VZO is below 0 and VZO is falling. We have a bearish trend. Most likely.
Rising means that VZO on close is higher than the previous day.
Falling means that VZO on close is lower than the previous day.
What if VZO is falling above 0 line?
It means we have a high probability of a bearish trend.
Thus the indicator returns 0 when falling above 0 (or rising bellow 0) and we combine higher and lower timeframes to gauge the trend.
In the next Image you can see that trend is positive on 4h, neutral on 12h and positive on 1D. That means trend is positive.
I am sorry, the chart is a bit messy. The idea is to use the indicator over more than 1 Timeframe.
What is approximation and smoothing?
They are mathematical concepts for making a discrete set of numbers a
continuous curved line.
Fourier and Wavelet approximation of a close price are taken from aprox library.
Key Features:
You can tailor the indicator to your preferences with adjustable parameters such as VZO length, noise reduction settings, and smoothing length.
Volume Zone Oscillator (VZO) shows market sentiment with the VZO, enhanced with Exponential Moving Average (EMA) smoothing for clearer trend identification.
Noise Reduction leverages Euler's White noise capabilities for effective noise reduction in the VZO, providing a cleaner and more accurate representation of market dynamics.
Choose between the traditional Fast Fourier Transform (FFT), the innovative Double Discrete Fourier Transform (DTF32) and Wavelet soothed Fourier soothed price series to suit your analytical needs.
Image of Wavelet transform with FAST settings, Double Fourier transform with FAST settings. Improved noice reduction with SLOW settings, and standard FSVZO with SLOW settings:
Fast setting are setting by default:
VZO length = 2
NoiceR max Length = 2
Slow settings are:
VZO length = 5 or 7
NoiceR max Length = 8
As you can see fast setting are more volatile. I suggest averaging fast setting on 4h 12h 1d 2d 3d 4d W and M Timeframe to get a clear view on market trend.
What if I want long only when VZO is rising and above 15 not 0?
You have set Setting VzoDifference to 15. That reduces the number of trend changes.
Example of W&FSVZO with VzoDifference 15 than 0:
VZO crossed 0 line but not 15 line and that's why Indicator returns 0 in one case an 1 in another.
What is Smooth length setting?
A way of calculating Bullish or Bearish FSVZO.
If smooth length is 2 the trend is rising if:
rising = VZO > ta.ema(VZO, 2)
Meaning that we check if VZO is higher that exponential average of the last 2 elements.
If smooth length is 1 the trend is rising if:
rising = VZO_ > VZO_
Rising is boolean value, meaning TRUE if rising and FALSE if falling.
Mathematical equations presented in Pinescript:
Fourier of the real (x axis) discrete:
x_0 = array.get(x, 0) + array.get(x, 1) + array.get(x, 2)
x_1 = array.get(x, 0) + array.get(x, 1) * math.cos( -2 * math.pi * _dir / 3 ) - array.get(y, 1) * math.sin( -2 * math.pi * _dir / 3 ) + array.get(x, 2) * math.cos( -4 * math.pi * _dir / 3 ) - array.get(y, 2) * math.sin( -4 * math.pi * _dir / 3 )
x_2 = array.get(x, 0) + array.get(x, 1) * math.cos( -4 * math.pi * _dir / 3 ) - array.get(y, 1) * math.sin( -4 * math.pi * _dir / 3 ) + array.get(x, 2) * math.cos( -8 * math.pi * _dir / 3 ) - array.get(y, 2) * math.sin( -8 * math.pi * _dir / 3 )
Euler's Noice reduction with both close and Discrete Furrier approximated price.
w = (dft1*src - dft1 *src ) / math.sqrt(math.pow(math.abs(src- src ),2) + math.pow(math.abs(dft1 - dft1 ),2))
filt := na(filt ) ? 0 : c1 * (w*dft1 + nz(w *dft1 )) / 2.0 /math.abs(dft1 -dft1 ) + c2 * nz(filt ) - c3 * nz(filt )
Usecase:
First option:
Select the preferred version of DFT and noise reduction settings based on your analysis requirements.
Leverage the script to identify Bullish and Bearish trends, shown with green and red triangle.
Combine Different Timeframes to accurately determine market trend.
Second option:
Pull the data with API sockets to automate your trading journey.
plot(close, title="ClosePrice", display=display.status_line)
plot(open, title="OpenPrice", display=display.status_line)
plot(greencon ? 1 : redcon ? -1 : 0, title="position", display=display.status_line)
Use ClosePrice, OpenPrice and "position" titles to easily read and backtest your strategy utilising more than 1 Time Frame.
Indicator id:
USER;e7a774913c1242c3b1354334a8ea0f3c
(only relevant to those that use API requests)
Trend Signals with TP & SL [UAlgo]The "Trend Signals with TP & SL " indicator is a versatile tool designed to assist traders in identifying potential trend continuation opportunities within financial markets Utilizing a combination of technical indicators and user-defined parameters, this indicator aims to provide clear and actionable signals to aid traders in making informed trading decisions.
🔶 Features:
Trend Continuation Signals : The indicator generates signals to identify potential trend continuation points based on the input parameters such as sensitivity, ATR length, and cloud moving average length.
Take-Profit and Stop-Loss Levels: It calculates and plots three levels of take-profit (1R, 2R, 3R) and stop-loss levels based on the entry price of the trade.
Short Position Example:
Long Position Example:
Visualization: The script visualizes the trend signals, entry points, take-profit levels, and stop-loss levels on the price chart, making it easier for traders to interpret the signals.
Alert System: The indicator includes an alert system that notifies the user when there is a change in trend direction or when a buy/sell signal is generated. The alerts provide essential information such as entry price, take-profit levels, and stop-loss levels.
🔶 Calculations :
Trend Calculation: Trend signals are determined based on the comparison between the current closing price and the upper and lower bounds calculated using the Average True Range (ATR) multiplied by a sensitivity factor. A trend is considered bullish if the closing price is above the upper bound and bearish if it's below the lower bound.
Entry, Stop Loss, and Take Profit Calculation: Entry points for long and short positions are identified when there's a change in trend direction.
Stop-loss levels are calculated as a percentage of the entry price, where users can define the percentage based on their risk tolerance.
Take-profit levels are calculated as multiples of the stop-loss level (1R, 2R, 3R).
Cloud Moving Averages: Simple moving averages (SMAs) are calculated for high and low prices over a specified period to create a "cloud" visualization on the chart.
MACD Clouds: Moving Average Convergence Divergence (MACD) indicator is used to determine the market's momentum and trend direction. Positive and negative clouds are plotted based on the MACD line and its signal line, indicating potential bullish or bearish trends.
Signal Generation: Buy and sell signals are generated based on specific conditions such as RSI, CMO (Chande Momentum Oscillator), and pivot points.
Signals are triggered when certain criteria are met, indicating potential opportunities for entering or exiting trades.
🔶 Disclaimer:
Use at Your Own Risk: Trading involves significant risk, and this script is provided for educational and informational purposes only. It does not guarantee profitable trades, and users should exercise caution and perform their own analysis before making trading decisions.
Parameter Sensitivity: The effectiveness of the indicator may vary depending on the chosen parameters, market conditions, and timeframe. Users are encouraged to backtest the script thoroughly and adjust the parameters according to their trading preferences.
Not Financial Advice: The information provided by this script should not be considered as financial advice. Users are solely responsible for their trading decisions and should consult with a qualified financial advisor if needed.
Backtesting and Validation: Before implementing this indicator in live trading, users are strongly encouraged to conduct rigorous backtesting and validation to assess its performance under various market conditions. Past performance is not indicative of future results, and users should carefully evaluate the effectiveness of the indicator based on their individual trading preferences and risk tolerance.
[imba]lance algo🟩 INTRODUCTION
Hello, everyone!
Please take the time to review this description and source code to utilize this script to its fullest potential.
🟩 CONCEPTS
This is a trend indicator. The trend is the 0.5 fibonacci level for a certain period of time.
A trend change occurs when at least one candle closes above the level of 0.236 (for long) or below 0.786 (for short). Also it has massive amout of settings and features more about this below.
With good settings, the indicator works great on any market and any time frame!
A distinctive feature of this indicator is its backtest panel. With which you can dynamically view the results of setting up a strategy such as profit, what the deposit size is, etc.
Please note that the profit is indicated as a percentage of the initial deposit. It is also worth considering that all profit calculations are based on the risk % setting.
🟩 FEATURES
First, I want to show you what you see on the chart. And I’ll show you everything closer and in more detail.
1. Position
2. Statistic panel
3. Backtest panel
Indicator settings:
Let's go in order:
1. Strategies
This setting is responsible for loading saved strategies. There are only two preset settings, MANUAL and UNIVERSAL. If you choose any strategy other than MANUAL, then changing the settings for take profits, stop loss, sensitivity will not bring any results.
You can also save your customized strategies, this is discussed in a separate paragraph “🟩HOW TO SAVE A STRATEGY”
2. Sensitive
Responsible for the time period in bars to create Fibonacci levels
3. Start calculating date
This is the time to start backtesting strategies
4. Position group
Show checkbox - is responsible for displaying positions
Fill checkbox - is responsible for filling positions with background
Risk % - is responsible for what percentage of the deposit you are willing to lose if there is a stop loss
BE target - here you can choose when you reach which take profit you need to move your stop loss to breakeven
Initial deposit- starting deposit for profit calculation
5. Stoploss group
Fixed stoploss % checkbox - If choosed: stoploss will be calculated manually depending on the setting below( formula: entry_price * (1 - stoploss percent)) If NOT choosed: stoploss will be ( formula: fibonacci level(0.786/0.236) * (1 + stoploss percent))
6. Take profit group
This group of settings is responsible for how far from the entry point take profits will be and what % of the position to fix
7. RSI
Responsible for configuring the built-in RSI. Suitable bars will be highlighted with crosses above or below, depending on overbought/oversold
8. Infopanels group
Here I think everything is clear, you can hide or show information panels
9. Developer mode
If enabled, all events that occur will be shown, for example, reaching a take profit or stop loss with detailed information about the unfixed balance of the position
🟩 HOW TO USE
Very simple. All you need is to wait for the trend to change to long or short, you will immediately see a stop loss and four take profits, and you will also see prices. Like in this picture:
🟩 ALERTS
There are 3 types of alerts:
1. Long signal
2. Short signal
3. Any alert() function call - will be send to you json with these fields
{
"side": "LONG",
"entry": "64.454",
"tp1": "65.099",
"tp2": "65.743",
"tp3": "66.388",
"tp4": "67.032",
"winrate": "35.42%",
"strategy": "MANUAL",
"beTargetTrigger": "1",
"stop": "64.44"
}
🟩 HOW TO SAVE A STRATEGY
First, you need to make sure that the “MANUAL” strategy is selected in the strategy settings.
After this, you can start selecting parameters that will show the largest profit in the statistics panel.
I have highlighted what you need to pay attention to when choosing a strategy
Let's assume you have set up a strategy. The main question is how to preserve it?
Let’s say the strategy turned out with the following parameters:
Next we need to find this section of code:
// STRATS
selector(string strategy_name) =>
strategy_settings = Strategy_settings.new()
switch strategy_name
"MANUAL" =>
strategy_settings.sensitivity := 18
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
"UNIVERSAL" =>
strategy_settings.sensitivity := 20
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
// "NEW STRATEGY" =>
// strategy_settings.sensitivity := 20
// strategy_settings.risk_percent := 1
// strategy_settings.break_even_target := "1"
// strategy_settings.tp1_percent := 1
// strategy_settings.tp1_percent_fix := 40
// strategy_settings.tp2_percent := 2
// strategy_settings.tp2_percent_fix := 30
// strategy_settings.tp3_percent := 3
// strategy_settings.tp3_percent_fix := 20
// strategy_settings.tp4_percent := 4
// strategy_settings.tp4_percent_fix := 10
// strategy_settings.fixed_stop := false
// strategy_settings.sl_percent := 0.0
strategy_settings
// STRATS
Let's uncomment on the latest strategy called "NEW STRATEGY" rename it to "SOL 5m" and change the sensitivity:
// STRATS
selector(string strategy_name) =>
strategy_settings = Strategy_settings.new()
switch strategy_name
"MANUAL" =>
strategy_settings.sensitivity := 18
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
"UNIVERSAL" =>
strategy_settings.sensitivity := 20
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
"SOL 5m" =>
strategy_settings.sensitivity := 15
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
strategy_settings
// STRATS
Now let's find this code:
strategy_input = input.string(title = "STRATEGY", options = , defval = "MANUAL", tooltip = "EN:\nTo manually configure the strategy, select MANUAL otherwise, changing the settings won't have any effect\nRU:\nЧтобы настроить стратегию вручную, выберите MANUAL в противном случае изменение настроек не будет иметь никакого эффекта")
And let's add our new strategy there, it turned out like this:
strategy_input = input.string(title = "STRATEGY", options = , defval = "MANUAL", tooltip = "EN:\nTo manually configure the strategy, select MANUAL otherwise, changing the settings won't have any effect\nRU:\nЧтобы настроить стратегию вручную, выберите MANUAL в противном случае изменение настроек не будет иметь никакого эффекта")
That's all. Our new strategy is now saved! It's simple! Now we can select it in the list of strategies:
Monthly Performance Table by Dr. MauryaWhat is this ?
This Strategy script is not aim to produce strategy results but It aim to produce monthly PnL performance Calendar table which is useful for TradingView community to generate a monthly performance table for Own strategy.
So make sure to read the disclaimer below.
Why it is required to publish?:
I am not satisfied with the monthly performance available on TV community script. Sometimes it is very lengthy in code and sometimes it showing the wrong PNL for current month.
So I have decided to develop new Monthly performance or return in value as well as in percentage with highly flexible to adjust row automatically.
Features :
Accuracy increased for current month PnL.
There are 14 columns and automatically adjusted rows according to available trade years/month.
First Column reflect the YEAR, from second column to 13 column reflect the month and 14 column reflect the yearly PnL.
In tabulated data reflects the monthly PnL (value and (%)) in month column and Yearly PnL (value and (%)) in Yearly column.
Various color input also added to change the table look like background color, text color, heading text color, border color.
In tabulated data, background color turn green for profit and red for loss.
Copy from line 54 to last line as it is in your strategy script.
Credit: This code is modified and top up of the open-source code originally written by QuantNomad. Thanks for their contribution towards to give base and lead to other developers. I have changed the way of determining past PnL to array form and keep separated current month and year PnL from array. Which avoid the false pnl in current month.
Strategy description:
As in first line I said This strategy is aim to provide monthly performance table not focused on the strategy. But it is necessary to explain strategy which I have used here. Strategy is simply based on ADX available on TV community script. Long entry is based on when the difference between DIPlus and ADX is reached on certain value (Set value in Long difference in Input Tab) while Short entry is based on when the difference between DIMinus and ADX is reached on certain value (Set value in Short difference in Input Tab).
Default Strategy Properties used on chart(Important)
This script backtest is done on 1 hour timeframe of NSE:Reliance Inds Future cahrt, using the following backtesting properties:
Balance (default): 500 000 (default base currency)
Order Size: 1 contract
Comission: 20 INR per Order
Slippage: 5 tick
Default setting in Input tab
Len (ADX length) : 14
Th (ADX Threshhold): 20
Long Difference (DIPlus - ADX) = 5
Short Difference (DIMinus - ADX) = 5
We use these properties to ensure a realistic preview of the backtesting system, do note that default properties can be different for various reasons described below:
Order Size: 1 contract by default, this is to allow the strategy to run properly on most instruments such as futures.
Comission: Comission can vary depending on the market and instrument, there is no default value that might return realistic results.
We strongly recommend all users to ensure they adjust the Properties within the script settings to be in line with their accounts & trading platforms of choice to ensure results from the strategies built are realistic.
Disclaimer:
This script not provide indicative of any future results.
This script don’t provide any financial advice.
This strategy is only for the readymade snippet code for monthly PnL performance calender table for any own strategy.
Personal Trading Hours (timezone Europe/Amsterdam)This Personal Trading Hours indicator is intended to specify the times you can trade and make them visible on the chart. Multiple sessions can be specified per specific day of the week and you can give each day its own color if you want.
This can be used perfectly if you are backtesting your strategy manually. You can indicate exactly when you have time to look at the charts and therefore only perform your backtest at those times. Making mistakes that you open en close trades during your sleeptime or worktime in your backtest are gone.
But this indicator is also suitable for live trading.
Filter out the times when you don't want to trade, for example during lunchtime, during opening hours of the exchanges or when you know that big news events will take place during your tradingweek. All the timesessions you do want to trade you can make visible on you chart.
The timezone that is used for this indicator is the timezone: Europe/Amsterdam and therefor only usable for traders in this timezone.
You can use this indicator for timeframes lower then the Daily timeframe with the normal settings. If you want to use this indicator on the Daily timeframe, all the settings in the upper part of the settingsmenu must be unchecked and only the part at the bottom of the settingsmenu can then be used.
This indicator doesn't work on timeframes higher than the Daily timeframe.
If you do not use all the tradingsessions on each day, you have to make sure that all the boxes are filled. So unused session boxes must have the same timeperiodes as the used boxes, otherwise the whole day will be highlighted on the chart.
[tradinghook] - Renko Trend Reversal Strategy V2Title: Renko Trend Reversal Strategy
Short Title: - Renko TRS
> Special thanks to for manually calculating `renkoClose` and `renkoOpen` values in order to remove the infamous repaint issue
Description:
The Renko Trend Reversal Strategy ( - Renko TRS) is a powerful and original trading approach designed to identify trend reversals in financial markets using Renko charts. Renko charts differ from traditional time-based charts, as they focus solely on price movements and ignore time, resulting in a clearer representation of market trends. This strategy leverages Renko charts in conjunction with the Average True Range (ATR) to capture trend reversals with high precision and effectiveness.
Key Concepts:
Renko Charts: Renko charts are unique chart types that only plot price movements beyond a predefined brick size, ignoring time and noise. By doing so, they provide a more straightforward depiction of market trends, eliminating insignificant price fluctuations and making it easier to spot trend reversals.
Average True Range (ATR): The strategy utilizes the ATR indicator, which measures market volatility and provides valuable insights into potential price movements. By setting the brick size of the Renko chart based on the ATR, the strategy adapts to changing market conditions, ensuring optimal performance across various instruments and timeframes.
How it Works:
The Renko Trend Reversal Strategy is designed to identify trend reversal points and generate buy or sell signals based on the following principles:
Renko Brick Generation: The strategy calculates the ATR over a user-defined period (ATR Length) and utilizes this value to determine the size of Renko bricks. Larger ATR values result in bigger bricks, capturing higher market volatility, while smaller ATR values create smaller bricks for calmer market conditions.
Buy and Sell Signals: The strategy generates buy signals when the Renko chart's open price crosses below the close price, indicating a potential bullish trend reversal. Conversely, sell signals are generated when the open price crosses above the close price, suggesting a bearish trend reversal. These signals help traders identify potential entry points to capitalize on market movements.
Stop Loss and Take Profit Management: To manage risk and protect profits, the strategy incorporates dynamic stop-loss and take-profit levels. The stop-loss level is calculated as a percentage of the Renko open price, ensuring a fixed risk amount for each trade. Similarly, the take-profit level is set as a percentage of the Renko open price to secure potential gains.
How to Use:
Inputs: Before using the strategy, traders can customize several parameters to suit their trading preferences. These inputs include the ATR Length, Stop Loss Percentage, Take Profit Percentage, Start Date, and End Date. Adjusting these settings allows users to optimize the strategy for different market conditions and risk tolerances.
Chart Setup: Apply the - Renko TRS script to your desired financial instrument and timeframe on TradingView. The Renko chart will dynamically adjust its brick size based on the ATR Length parameter.
Buy and Sell Signals: The strategy will generate green "Buy" labels below bullish reversal points and red "Sell" labels above bearish reversal points on the Renko chart. These labels indicate potential entry points for long and short trades, respectively.
Risk Management: The strategy automatically calculates stop-loss and take-profit levels based on the user-defined percentages. Traders can ensure proper risk management by using these levels to protect their capital and secure profits.
Backtesting and Optimization: Before implementing the strategy live, traders are encouraged to backtest it on historical data to assess its performance across various market conditions. Adjust the input parameters through optimization to find the most suitable settings for specific instruments and timeframes.
Conclusion:
The - Renko Trend Reversal Strategy is a unique and versatile tool for traders looking to identify trend reversals with greater accuracy. By combining Renko charts and the Average True Range (ATR) indicator, this strategy adapts to market dynamics and provides clear entry and exit signals. Traders can harness the power of Renko charts while effectively managing risk through stop-loss and take-profit levels. Before using the strategy in live trading, backtesting and optimization will help traders fine-tune the parameters for optimal performance. Start exploring trend reversals with the - Renko TRS and take your trading to the next level.
(Note: This description is for illustrative purposes only and does not constitute financial advice. Traders are advised to thoroughly test the strategy and exercise sound risk management practices when trading in real markets.)
[tradinghook] - Renko Trend Reversal Strategy - Renko Trend Reversal Strategy
Short Title: - Renko TRS
Description:
The Renko Trend Reversal Strategy ( - Renko TRS) is a powerful and original trading approach designed to identify trend reversals in financial markets using Renko charts. Renko charts differ from traditional time-based charts, as they focus solely on price movements and ignore time, resulting in a clearer representation of market trends. This strategy leverages Renko charts in conjunction with the Average True Range (ATR) to capture trend reversals with high precision and effectiveness.
Key Concepts:
Renko Charts: Renko charts are unique chart types that only plot price movements beyond a predefined brick size, ignoring time and noise. By doing so, they provide a more straightforward depiction of market trends, eliminating insignificant price fluctuations and making it easier to spot trend reversals.
Average True Range (ATR): The strategy utilizes the ATR indicator, which measures market volatility and provides valuable insights into potential price movements. By setting the brick size of the Renko chart based on the ATR, the strategy adapts to changing market conditions, ensuring optimal performance across various instruments and timeframes.
How it Works:
The Renko Trend Reversal Strategy is designed to identify trend reversal points and generate buy or sell signals based on the following principles:
Renko Brick Generation: The strategy calculates the ATR over a user-defined period (ATR Length) and utilizes this value to determine the size of Renko bricks. Larger ATR values result in bigger bricks, capturing higher market volatility, while smaller ATR values create smaller bricks for calmer market conditions.
Buy and Sell Signals: The strategy generates buy signals when the Renko chart's open price crosses below the close price, indicating a potential bullish trend reversal. Conversely, sell signals are generated when the open price crosses above the close price, suggesting a bearish trend reversal. These signals help traders identify potential entry points to capitalize on market movements.
Stop Loss and Take Profit Management: To manage risk and protect profits, the strategy incorporates dynamic stop-loss and take-profit levels. The stop-loss level is calculated as a percentage of the Renko open price, ensuring a fixed risk amount for each trade. Similarly, the take-profit level is set as a percentage of the Renko open price to secure potential gains.
How to Use:
Inputs: Before using the strategy, traders can customize several parameters to suit their trading preferences. These inputs include the ATR Length, Stop Loss Percentage, Take Profit Percentage, Start Date, and End Date. Adjusting these settings allows users to optimize the strategy for different market conditions and risk tolerances.
Chart Setup: Apply the - Renko TRS script to your desired financial instrument and timeframe on TradingView. The Renko chart will dynamically adjust its brick size based on the ATR Length parameter.
Buy and Sell Signals: The strategy will generate green "Buy" labels below bullish reversal points and red "Sell" labels above bearish reversal points on the Renko chart. These labels indicate potential entry points for long and short trades, respectively.
Risk Management: The strategy automatically calculates stop-loss and take-profit levels based on the user-defined percentages. Traders can ensure proper risk management by using these levels to protect their capital and secure profits.
Backtesting and Optimization: Before implementing the strategy live, traders are encouraged to backtest it on historical data to assess its performance across various market conditions. Adjust the input parameters through optimization to find the most suitable settings for specific instruments and timeframes.
Conclusion:
The - Renko Trend Reversal Strategy is a unique and versatile tool for traders looking to identify trend reversals with greater accuracy. By combining Renko charts and the Average True Range (ATR) indicator, this strategy adapts to market dynamics and provides clear entry and exit signals. Traders can harness the power of Renko charts while effectively managing risk through stop-loss and take-profit levels. Before using the strategy in live trading, backtesting and optimization will help traders fine-tune the parameters for optimal performance. Start exploring trend reversals with the - Renko TRS and take your trading to the next level.
(Note: This description is for illustrative purposes only and does not constitute financial advice. Traders are advised to thoroughly test the strategy and exercise sound risk management practices when trading in real markets.)
