NFIX5_analysis.md

NostalgiaForInfinityX5 analysis (WIP)

Configuration

Indicators

Entry protections

1. global_protections_long_pump
2. global_protections_short_pump

Entry signals

Long signals

Normal mode

1. Condition 1 - Normal mode (Long)
2. Condition 2 - Normal mode (Long)
3. Condition 3 - Normal mode (Long)
4. Condition 4 - Normal mode (Long)
5. Condition 5 - Normal mode (Long)
6. Condition 6 - Normal mode (Long)

Quick mode

8. Condition 42 - Quick mode (Long)
9. Condition 43 - Quick mode (Long)

Rapid mode

10. Condition 101 - Rapid mode (Long)

Grind mode

11. Condition 120 - Grind mode (Long)

Top coins mode

13. Condition 143 - Top Coins mode (Long)

Short signals

Normal mode

13. Condition 501 - Normal mode (Short)
14. Condition 502 - Normal mode (Short)
15. Condition 503 - Normal mode (Short)
16. Condition 504 - Normal mode (Short)

Quick and rapid mode

18. Condition 542 - Quick mode (Short)
19. Condition 543 - Rapid mode (Short)

Top coins mode

21. Condition 641 - Top Coins mode (Short)
22. Condition 642 - Top Coins mode (Short)

Position adjustment modes

1. Grind
2. Rebuy
3. Derisk

Exit signals

1. Tags
2. Long exit normal function
   1. long_exit_signals function
   2. long_exit_main function
   3. long_exit_williams_r function
   4. long_exit_dec function
   5. long_exit_stoploss

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Config.json

Keys that can be added to config.json

"exit_profit_only"                  # Default: False
"num_cores_indicators_calc"         # Default: 0. Number of cores to use for pandas_ta indicators calculations.
"custom_fee_open_rate"              # Default: trade.fee_open
"custom_fee_close_rate"             # Default: trade.fee_close
"futures_mode_leverage"             # Default: 3.0
"futures_mode_leverage_rebuy_mode"  # Default: 3.0
"futures_mode_leverage_grind_mode"  # Default: 3.0
"stop_threshold_doom_spot"          # Default: 0.25
"stop_threshold_doom_futures"       # Default: 0.60
"derisk_enable"                     # Default: True
"regular_mode_derisk_spot"          # Default: -0.24
"regular_mode_derisk_1_spot"        # Default: -0.24
"regular_mode_derisk_futures"       # Default: -0.60
"regular_mode_derisk_1_futures"     # Default: -0.60
"grind_mode_max_slots"              # Default: 1
"grind_mode_coins"                  # Default: ["MATIC", "ADA", "ARB", "DOT", "XLM", "ALGO", "ETH", "RNDR", "XMR", "AVAX", "NEAR", "DOGE", "BCH", "ETC", "FTM", "KAS", "HBAR", "SUI", "TON", "XRP", "UNI", "LTC", "FIL", "ATOM", "GRT", "LINK", "VET", "THETA", "EOS", "LRC", "QTUM", "CELR" ]
"max_slippage"                     # Default: 0.012

  

Indicators

The strategy outlined utilizes a variety of technical indicators to analyze market trends and provide signals for trading. Here’s a breakdown of each type of indicator and its purpose:

1. RSI (Relative Strength Index)

• Purpose: Measures the speed and change of price movements to identify overbought or oversold conditions.
• Usage: Different periods are used:
  • RSI_3 & RSI_4: Very short-term momentum indicators for quick responses.
  • RSI_14: Standard period, balances between short-term and long-term trends.
  • RSI_20: Longer-term view for trend analysis.
• Additional Calculation: Change percentages (RSI_3_change_pct, RSI_14_change_pct) assess the momentum's rate of change.

2. EMA (Exponential Moving Average)

• Purpose: Smooths price data to identify trends, giving more weight to recent prices.
• Usage: Various lengths are applied to monitor short-term to long-term trends:
  • Short-term: EMA_3, EMA_9, EMA_12
  • Medium-term: EMA_16, EMA_20, EMA_26
  • Long-term: EMA_50, EMA_200

3. SMA (Simple Moving Average)

• Purpose: Provides a smoothed view of price trends, giving equal weight to all data points.
• Usage: Fewer lengths are used, such as SMA_16 and SMA_30, for medium-term trend analysis.

4. Bollinger Bands (BB)

• Purpose: Measure price volatility and potential overbought/oversold levels.
• Usage:
  • BBL (Lower Band): Support level.
  • BBM (Middle Band): Moving average baseline.
  • BBU (Upper Band): Resistance level.
  • BBB (Bandwidth): Indicates the range of price volatility.
  • BBP (Percent B): Position of the price within the bands.
  • Applied to 20- and 40-period lengths.

5. MFI (Money Flow Index)

• Purpose: Combines price and volume data to assess the strength of money flowing in or out of an asset.
• Usage: Standard 14-period calculation.

6. CMF (Chaikin Money Flow)

• Purpose: Analyzes the volume-weighted average of accumulation and distribution.
• Usage: Applied with a 20-period length.

7. Williams %R

• Purpose: Identifies overbought and oversold levels similar to RSI but on a reversed scale.
• Usage:
  • WILLR_14: Shorter-term trend.
  • WILLR_480: Longer-term trend for broader market context.

8. AROON

• Purpose: Measures the time since the highest high or lowest low to identify trend direction and strength.
• Usage:
  • AROONU: Uptrend strength.
  • AROOND: Downtrend strength.

9. Stochastic RSI (STOCHRSI)

• Purpose: A momentum indicator that applies stochastic calculations to RSI values to refine overbought/oversold signals.
• Usage: Tracks %K and %D lines.

10. KST (Know Sure Thing)

• Purpose: A momentum oscillator that combines multiple rate-of-change calculations to smooth out signals.
• Usage:
  • KST: The primary trend signal.
  • KSTs: Smoothed version for confirmation.

11. OBV (On-Balance Volume)

• Purpose: Relates volume to price changes to assess buying and selling pressure.
• Usage:
  • Includes percentage changes in OBV for momentum analysis.

12. ROC (Rate of Change)

• Purpose: Measures the percentage change in price over a specific period to identify momentum.
• Usage:
  • Short-term: ROC_2
  • Medium-term: ROC_9

13. Candle Change Percent

• Purpose: Measures the percentage change between the open and close prices to gauge intraday momentum.

14. Close Max

• Purpose: Tracks the maximum closing price over a rolling period.
• Usage:
  • close_max_48: Applied to a 48-period window.

15. Empty Candle Count

• Purpose: Counts the number of zero-volume candles over a specified window to identify illiquid periods.
• Usage:
  • num_empty_288: Rolling sum over 288 periods.

Entry protections

global_protections_long_pump

The "global_protections_long_pump" in this freqtrade strategy appears to be a pre-signal filter designed to analyze various market indicators across multiple timeframes to detect and prevent potentially unfavorable conditions for entering long positions. This protection mechanism uses a combination of technical indicators like RSI, AROON, CCI, Stochastic RSI, and ROC across 5-minute, 15-minute, hourly, 4-hour, and daily charts.

Summary of its logic:

• It evaluates downtrends or overbought conditions across different timeframes.
• It checks if indicators are not yet low enough to signify a safe entry (e.g., RSI above certain thresholds, Stochastic RSI indicating overbought conditions).
• It analyzes whether higher timeframes like 1-day and 4-hour charts are still in a high or overbought state despite short-term pullbacks.

This filter aims to avoid entering long trades when market conditions are not favorable or could indicate a potential pump or overbought scenario, which might lead to a rapid reversal. By applying this pre-check, the strategy ensures a more cautious approach before committing to signals from other parts of the strategy.

Potential improvements

The "global_protections_long_pump" filter is comprehensive, analyzing multiple indicators across various timeframes to assess market conditions. While it's robust, here are some suggestions for potential improvement or refinement to optimize its performance:

1. Indicator Thresholds

• Observation: Many thresholds (e.g., RSI > 5 or < 30) are hardcoded and may not dynamically adapt to changing market conditions.
• Suggestion:
  • Use dynamic thresholds based on historical volatility or market state. For example, calculate rolling averages or deviations for RSI thresholds instead of fixed values.
  • Incorporate an adaptive threshold mechanism that adjusts depending on the asset's ATR (Average True Range) or other volatility metrics.

2. Redundancy of Conditions

• Observation: There are overlapping checks for the same indicators across different timeframes (e.g., RSI_3 on multiple timeframes with similar thresholds).
• Suggestion:
  • Consolidate redundant conditions to reduce complexity without sacrificing the filter’s effectiveness.
  • Use aggregated signals, such as weighted averages of RSI or other indicators across timeframes, instead of evaluating each timeframe independently.

3. Trend Confirmation

• Observation: The logic heavily focuses on overbought/oversold conditions but does not explicitly account for broader trend confirmation.
• Suggestion:
  • Introduce trend-following indicators like EMA or SMA crossovers to ensure alignment with the prevailing trend.
  • Use ADX (Average Directional Index) to confirm the strength of the trend and only activate the filter during trending markets.

4. Handling Multi-Timeframe Conflicts

• Observation: Different timeframes may provide conflicting signals, which can lead to unclear filter activation.
• Suggestion:
  • Implement a weighting system to prioritize longer timeframes (e.g., daily charts) when conflicts arise.
  • Define explicit rules to resolve conflicts between timeframes, such as "if 4h is neutral but 15m is overbought, defer to the higher timeframe."

5. Improved Overbought Detection

• Observation: The conditions focus heavily on individual indicator thresholds (e.g., Stochastic RSI, ROC) but may miss nuanced market behavior.
• Suggestion:
  • Combine indicators into composite signals, such as creating a combined momentum score using RSI, Stochastic RSI, and AROON.
  • Integrate Bollinger Bands or Keltner Channels to identify overbought conditions relative to price volatility.

6. Exit Condition Verification

• Observation: The filter ensures entry prevention during risky conditions but lacks explicit alignment with exit strategies.
• Suggestion:
  • Integrate this protection with the strategy's exit logic to avoid scenarios where entries are filtered out but existing positions remain exposed to adverse conditions.

7. Performance Evaluation

• Observation: The logic is detailed but may add significant computational overhead.
• Suggestion:
  • Profile the execution time of this filter to identify bottlenecks. Optimize indicator calculations (e.g., caching intermediate results).
  • Regularly backtest and validate the filter against historical data to ensure its efficacy, especially in varying market regimes (bullish, bearish, and sideways).

8. Incorporate Volume Analysis

• Observation: The filter does not include volume-based indicators, which can provide critical insights into market strength.
• Suggestion:
  • Add volume-related metrics like OBV (On-Balance Volume) or CMF (Chaikin Money Flow) to detect pumps with unusual volume spikes.
  • Use volume divergence in combination with other conditions to enhance filter reliability.

9. Simplify for Maintainability

• Observation: The filter’s complexity might make it harder to maintain or debug.
• Suggestion:
  • Modularize the conditions into smaller, reusable blocks (e.g., separate logic for each timeframe or indicator group).
  • Add comments and clear documentation to explain each condition's rationale.

By implementing these improvements, the "global_protections_long_pump" filter can become more adaptive, efficient, and effective in identifying unfavorable market conditions while reducing complexity and redundancy.

Downtrend detection improvements

The "global_protections_long_pump" filter has some mechanisms in place that can help in downtrend conditions, but it is not fully optimized for sustained or sharp downtrends. Here’s an analysis:

Strengths for Downtrend Detection:

1. Overbought Indicators Across Timeframes:

   • The filter uses indicators like RSI, Stochastic RSI, and AROON to detect overbought conditions, which can help avoid entering long positions during downtrend pullbacks.

2. Multi-Timeframe Analysis:

   • By considering multiple timeframes (e.g., 5m, 15m, 1h, 4h, and 1d), the filter can potentially identify broader downtrends and prevent false signals from short-term countertrend moves.

3. Momentum Indicators:

   • Indicators like ROC and WILLR (Williams %R) are included, which can capture bearish momentum and avoid entering trades during periods of strong selling pressure.

4. Redundancy Across Indicators:

   • The combination of multiple conditions ensures that even if one indicator fails to detect the downtrend, others may trigger the protection.

Limitations in Handling Downtrends:

1. Insufficient Focus on Downtrend Confirmation:

   • While the filter avoids overbought conditions, it does not explicitly confirm the presence of a downtrend (e.g., via moving average crossovers or ADX).
   • It lacks checks for persistent lower lows and lower highs, which are classic downtrend signals.

2. Limited Adaptation to Strong Bearish Momentum:

   • Fixed thresholds (e.g., RSI > 5 or ROC < certain values) might not adapt well to different levels of bearish momentum.
   • Sharp or volatile downtrends might bypass the filter if they don’t meet the pre-defined thresholds.

3. Conflict Between Timeframes:

   • The filter might give conflicting signals when lower timeframes show a temporary pullback in the downtrend, potentially allowing entries when the overall market is bearish.

4. Volume and Market Context:

   • There is no volume analysis or integration of bearish divergence, which are critical in understanding the strength of a downtrend.

Suggestions for Improving Downtrend Readiness:

1. Integrate Trend-Confirming Indicators:

   • Add moving average crossovers (e.g., 50 EMA below 200 EMA) or ADX > 25 to explicitly confirm downtrend conditions.
   • Use price action patterns, such as detecting a series of lower lows and lower highs.

2. Dynamic Thresholds:

   • Replace fixed indicator thresholds with dynamic ones based on market volatility (e.g., ATR) to better adapt to strong selling pressure.

3. Incorporate Volume Analysis:

   • Use OBV (On-Balance Volume) or Chaikin Money Flow to detect selling pressure accompanying price declines.

4. Define Clear Timeframe Priority:

   • In downtrends, prioritize higher timeframes (e.g., 4h or 1d) to avoid being misled by temporary pullbacks in shorter timeframes.

5. Downtrend-Specific Protections:

   • Add specific protections for prolonged downtrends, such as avoiding trades when key moving averages slope downward or when ROC consistently indicates strong negative momentum.

Conclusion:

The filter is reasonably prepared for avoiding long trades in some downtrend scenarios due to its focus on overbought conditions and multi-timeframe analysis. However, it lacks explicit mechanisms to confirm and adapt to strong or persistent bearish trends. Enhancing the filter with trend-confirming indicators, dynamic thresholds, and volume-based analysis would make it more robust in downtrend conditions.

global_protections_short_pump

TODO

Entry signals

Condition #1 - Normal mode (Long)

Analysis of the Entry Signal

1. df["EMA_26"] > df["EMA_12"]

   • Meaning: The 26-period EMA is greater than the 12-period EMA.
   • Interpretation: This indicates that the broader trend is bearish, as the slower-moving EMA is above the faster one. The condition ensures that the strategy aligns with the current downtrend.

2. (df["EMA_26"] - df["EMA_12"]) > (df["open"] * 0.030)

   • Meaning: The difference between the 26-period and 12-period EMAs is greater than 3% of the opening price.
   • Interpretation: This highlights a strong bearish trend with a significant gap between the EMAs, ensuring that the signal only triggers in pronounced downward momentum scenarios.

3. (df["EMA_26"].shift() - df["EMA_12"].shift()) > (df["open"] / 100.0)

   • Meaning: The EMA difference from the previous candle is greater than 1% of the opening price.
   • Interpretation: This condition confirms the consistency of the bearish trend over consecutive candles, avoiding false signals in choppy markets.

4. df["close"] < (df["BBL_20_2.0"] * 0.999)

   • Meaning: The closing price is below 99.9% of the lower Bollinger Band (calculated with a 20-period moving average and a 2-standard deviation band).
   • Interpretation: This indicates extreme bearish pressure, as the price is trading outside the lower Bollinger Band, suggesting oversold conditions or strong selling activity.

Overall Strategy Analysis

This signal seeks long entries during oversold conditions in a strongly bearish market, by combining:

• Trend Confirmation: The EMA relationship and gap thresholds ensure alignment with a well-defined bearish trend.
• Oversold Conditions: The closing price below the Bollinger Band highlights extreme selling pressure, making it a potential reversal point.

Strengths

1. Strong Trend Confirmation: By using EMA relationships and thresholds, the signal only triggers in pronounced bearish conditions, reducing the likelihood of false positives.
2. Oversold Market Indicator: The Bollinger Band condition adds a reversal component, identifying extreme conditions where a rebound might occur.
3. Robust Filtering: The multi-condition approach helps filter out weaker signals and focuses on higher-probability setups.

Suggestions for Improvement or Consideration

1. Volume Confirmation: Add a volume-based condition (e.g., high volume during the close below Bollinger Band) to validate the signal.
2. Dynamic EMA Thresholds: Consider adjusting the 0.030 multiplier dynamically based on recent volatility to improve adaptability.
3. Exit Strategy: Define clear profit-taking or stop-loss rules, such as targeting the middle Bollinger Band (mean reversion) or the EMA 12 level.
4. Add Reversal Triggers: Include a bullish confirmation signal (e.g., RSI crossing above a threshold) to improve timing and reduce entry risk.
5. Backtesting: Test the impact of values like 0.030 and 0.999 on performance across various assets and time frames to ensure optimal results.

Conclusion

This signal combines trend-following and mean-reversion components, designed for identifying oversold conditions in bearish markets. While robust, adding volume confirmation and dynamic thresholds could further enhance its reliability.

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Condition #2 - Normal mode (Long)

Analysis of the Entry Signal

1. df["AROONU_14"] < 25.0

   • Meaning: The 14-period Aroon Up is below 25.
   • Interpretation: This indicates a lack of recent highs, reflecting weak upward momentum and suggesting that the market is in a bearish phase or consolidation.

2. df["STOCHRSIk_14_14_3_3"] < 20.0

   • Meaning: The Stochastic RSI is below 20, signaling oversold conditions.
   • Interpretation: This suggests that the price has moved to an extreme low within its recent range, potentially indicating a reversal opportunity as selling pressure could be exhausted.

3. df["close"] < (df["EMA_20"] * 0.944)

   • Meaning: The closing price is below 94.4% of the 20-period EMA.
   • Interpretation: This reflects significant bearish pressure, as the price is trading well below a key moving average. It further reinforces the oversold nature of the market, highlighting a potential reversal zone.

Overall Strategy Analysis

This signal is designed to identify long entry points in oversold conditions within a bearish or consolidating market, relying on:

• Momentum Weakness: The Aroon Up and Stochastic RSI both confirm that bullish momentum is absent and the market is oversold.
• Bearish Price Context: The close being significantly below the EMA 20 suggests strong bearish sentiment, providing a discount entry opportunity if the trend reverses.

Strengths

1. Momentum and Oversold Confluence: Combining the Aroon Up and Stochastic RSI ensures that the signal aligns with both weak momentum and oversold conditions, increasing the likelihood of a successful reversal.
2. Clear Price Threshold: The EMA 20 condition filters entries based on bearish price action, ensuring alignment with a defined price context.
3. Simplicity: This setup uses straightforward conditions that are computationally efficient and easy to interpret.

Suggestions for Improvement or Consideration

1. Volume Confirmation: Adding a volume condition (e.g., high or increasing volume near the signal) could enhance confidence in a potential reversal.
2. Trend Filter: Consider incorporating a broader trend filter (e.g., EMA 50 > EMA 200) to ensure the signal aligns with the overall market direction.
3. Dynamic EMA Multiplier: Adjust the 0.944 factor based on market volatility or average true range (ATR) to make the condition adaptable to different market environments.
4. Exit Strategy: Define a clear exit plan, such as targeting the EMA 20 for mean reversion or setting a percentage-based stop-loss to manage risk.
5. Backtesting: Test various thresholds for the Aroon, Stochastic RSI, and EMA multiplier to identify optimal values for different assets and time frames.

Conclusion

This signal is a reversal-based strategy, leveraging oversold conditions and bearish price action to identify potential long entry points. While effective in its simplicity, adding volume-based validation and dynamic thresholds could further refine its performance.

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Condition #3 - Normal mode (Long)

Analysis of the Entry Signal

1. df["RSI_20"] < df["RSI_20"].shift(1)

   • Meaning: The 20-period RSI is decreasing compared to the previous period.
   • Interpretation: This indicates that momentum is weakening, with price action showing a lack of bullish strength. The condition reflects ongoing bearish sentiment.

2. df["RSI_4"] < 46.0

   • Meaning: The 4-period RSI is below 46, suggesting short-term weakness in price momentum.
   • Interpretation: This reinforces the bearish bias, as the short-term momentum indicator remains below a neutral level (typically 50), signaling that sellers are still in control.

3. df["AROONU_14"] < 25.0

   • Meaning: The 14-period Aroon Up is below 25.
   • Interpretation: This suggests that recent highs are weak or absent, reflecting a lack of bullish activity and strengthening the bearish sentiment.

4. df["close"] < df["SMA_16"] * 0.942

   • Meaning: The closing price is below 94.2% of the 16-period simple moving average (SMA).
   • Interpretation: This confirms that the price is trading well below its short-term average, highlighting significant bearish pressure and signaling a possible discount for long entry if conditions reverse.

Overall Strategy Analysis

This signal is designed to identify long entry points during bearish or oversold conditions, relying on:

• Momentum Weakness: Both RSI indicators and the Aroon Up confirm weak momentum and bearish conditions.
• Price Context: The close being far below the SMA 16 provides a measure of undervaluation, reinforcing the potential for a rebound.

Strengths

1. Momentum-Based Filtering: The combination of two RSI indicators ensures the signal captures both long-term and short-term bearish momentum, improving accuracy.
2. Trend Confirmation: The Aroon Up condition filters out situations where bullish activity might resume prematurely.
3. Price Undervaluation: The SMA multiplier ensures the entry occurs only at a significant discount, increasing the likelihood of entering near a potential reversal zone.

Suggestions for Improvement or Consideration

1. Trend Alignment: Add a higher timeframe trend filter (e.g., EMA 50 > EMA 200) to avoid countertrend entries in strong bear markets.
2. Dynamic SMA Multiplier: Adjust the 0.942 factor dynamically using recent volatility (e.g., ATR) to enhance adaptability across different market conditions.
3. Volume Condition: Include a volume-based filter to ensure that the signal aligns with increased trading activity, improving its reliability.
4. Exit Strategy: Define clear exit rules, such as targeting the SMA 16 for mean reversion or a percentage-based profit target and stop-loss.
5. Backtesting: Experiment with different thresholds for RSI, Aroon, and SMA multipliers to identify optimal settings for specific assets and time frames.

Conclusion

This signal leverages momentum weakness and undervaluation to identify long entry points in bearish market conditions. Its simplicity and focus on multiple indicators make it robust, but enhancements like trend alignment, volume filters, and dynamic thresholds could further improve its effectiveness.

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Condition #4 - Normal mode (Long)

Analysis of the Entry Signal

1. df["AROONU_14"] < 25.0

   • Meaning: The 14-period Aroon Up is below 25.
   • Interpretation: This indicates weak bullish momentum or the absence of recent highs, suggesting that the market is in a bearish phase or consolidation.

2. df["AROONU_14_15m"] < 25.0

   • Meaning: The 14-period Aroon Up on the 15-minute timeframe is also below 25.
   • Interpretation: This confirms that weak bullish momentum is consistent across both the current and a higher timeframe, reinforcing the bearish bias.

3. df["close"] < (df["EMA_9"] * 0.942)

   • Meaning: The closing price is below 94.2% of the 9-period Exponential Moving Average (EMA).
   • Interpretation: This reflects significant short-term bearish pressure, indicating the price is trading at a discount relative to its recent average.

4. df["close"] < (df["EMA_20"] * 0.960)

   • Meaning: The closing price is below 96% of the 20-period EMA.
   • Interpretation: This further validates the bearish trend over a slightly longer timeframe, suggesting a strong oversold condition and potential reversal zone.

Overall Strategy Analysis

This signal focuses on identifying long entry points in oversold conditions across multiple timeframes, leveraging:

• Momentum Weakness: The Aroon Up indicators confirm bearish momentum across both the current and higher timeframe.
• Bearish Price Context: The close being well below both the 9-period and 20-period EMAs reinforces the idea of significant price undervaluation.

Strengths

1. Multi-Timeframe Analysis: Incorporating the 15-minute Aroon Up ensures consistency in the bearish signal across timeframes, increasing the signal's reliability.
2. Dynamic Price Undervaluation: Using two EMAs with different multipliers provides a nuanced measure of bearish pressure and helps identify potential reversal zones.
3. Momentum Confirmation: The dual Aroon conditions ensure that momentum is consistently weak, avoiding premature long entries.

Suggestions for Improvement or Consideration

1. Volume Condition: Add a volume-based filter (e.g., above average volume) to confirm that the signal aligns with increased market participation.
2. Trend Filter: Implement a higher timeframe trend filter (e.g., EMA 50 > EMA 200) to avoid countertrend trades in strong bear markets.
3. Dynamic Multipliers: Adjust the 0.942 and 0.960 multipliers based on recent volatility (e.g., ATR) to enhance adaptability across different market conditions.
4. Exit Plan: Define exit rules, such as targeting the EMA 9 or EMA 20 for mean reversion or setting a percentage-based stop-loss and profit target.
5. Backtesting: Optimize the thresholds for Aroon, EMA multipliers, and timeframes to determine their effectiveness for specific assets and market conditions.

Conclusion

This signal is well-suited for capturing oversold conditions with multi-timeframe confirmation, using momentum weakness and price undervaluation as its core components. While robust in design, incorporating volume and trend alignment could further improve its reliability and performance.

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Condition 5 - Normal mode (Long)

Analysis of the Entry Signal

1. df["RSI_3"] < 50.0

   • Meaning: The 3-period RSI is below 50.
   • Interpretation: This indicates weakening momentum in the very short term. The RSI below 50 suggests that bearish pressure is slightly dominant, but it's not yet in oversold territory.

2. df["AROONU_14"] < 25.0

   • Meaning: The 14-period Aroon Up is below 25.
   • Interpretation: This shows a lack of recent highs, signaling weakening upward momentum.

3. df["AROOND_14"] > 75.0

   • Meaning: The 14-period Aroon Down is above 75.
   • Interpretation: A high Aroon Down value indicates that recent lows are dominating, further reinforcing bearish momentum.

4. df["STOCHRSIk_14_14_3_3"] < 30.0

   • Meaning: The Stochastic RSI is below 30.
   • Interpretation: This indicates oversold conditions, suggesting that the market might be nearing a reversal.

5. df["EMA_26"] > df["EMA_12"]

   • Meaning: The 26-period EMA is greater than the 12-period EMA.
   • Interpretation: This reflects a bearish trend in the medium term, as the slower EMA (26) is above the faster EMA (12). This condition ensures that the strategy only triggers in a broader bearish environment.

6. (df["EMA_26"] - df["EMA_12"]) > (df["open"] * 0.020)

   • Meaning: The difference between the 26-period and 12-period EMAs is greater than 2% of the opening price.
   • Interpretation: This condition ensures that there is a significant gap between the two EMAs, confirming a strong bearish trend.

7. (df["EMA_26"].shift() - df["EMA_12"].shift()) > (df["open"] / 100.0)

   • Meaning: The difference between the 26-period and 12-period EMAs in the previous candle is greater than 1% of the opening price.
   • Interpretation: This reinforces the current EMA condition by requiring a consistent bearish trend across consecutive candles.

Overall Strategy Analysis

This signal is tailored to identify long entry points in oversold conditions within a bearish trend, characterized by:

• Momentum Weakness: Multiple indicators (RSI, Aroon Up, Aroon Down, and Stochastic RSI) confirm that the market is oversold and dominated by bearish momentum.
• Bearish Trend Confirmation: The EMA conditions ensure that the overall trend is bearish, adding a layer of protection against entering too early in a potential trend reversal.

Strengths

1. Comprehensive Trend Analysis: By incorporating both momentum indicators (RSI, Aroon, and Stochastic RSI) and moving average crossovers, the signal ensures alignment with both trend and reversal conditions.
2. Multi-Condition Filtering: The EMA gap thresholds provide additional confirmation, filtering out weaker trends and improving reliability.

Suggestions for Improvement or Consideration

1. Exit Strategy: Include a clear exit plan, such as targeting a return to the EMA 12 level or setting fixed profit and stop-loss thresholds.
2. Dynamic Thresholds: Consider making the EMA gap conditions dynamic based on market volatility to adapt to different trading environments.
3. Volume Confirmation: Add a condition for high or increasing volume to validate the signal’s reliability, especially in oversold conditions.
4. Backtesting: Optimize values like 0.020 for the EMA gap and 50.0 for the RSI_3 threshold to ensure the signal is tuned for your asset and time frame.

Conclusion

This signal is well-suited for counter-trend trading in bearish environments. It provides a robust framework for identifying oversold conditions, but the reliance on bearish trends (EMA_26 > EMA_12) suggests it works best in markets with predictable reversals rather than chaotic price action.

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Condition 6 - Normal mode (Long)

Analysis of the Entry Signal

1. df["RSI_20"] < df["RSI_20"].shift(1)

   • Meaning: The 20-period RSI is decreasing compared to the previous candle.
   • Interpretation: This indicates a weakening momentum, which could signal either a pullback in an uptrend or a continuation of a downtrend.

2. df["RSI_4"] < 46.0

   • Meaning: The 4-period RSI is below 46.
   • Interpretation: A low RSI typically indicates oversold conditions or market weakness. However, 46 is not close to the usual oversold threshold (<30), suggesting the strategy is targeting subtle moves rather than extreme conditions.

3. df["AROONU_14"] < 25.0

   • Meaning: The 14-period Aroon Up is below 25.
   • Interpretation: A low Aroon Up value means the market hasn’t made a new high recently, potentially signaling a weakening uptrend. This aligns with the strategy’s intent to detect a market that has lost upward momentum.

4. df["close"] < df["SMA_16"] * 0.942

   • Meaning: The current closing price is below 94.2% of the 16-period simple moving average.
   • Interpretation: This indicates that the price is significantly below a recent average, suggesting potential oversold conditions or market weakness.

Overall Strategy Analysis

This combination of conditions seeks to identify long entries in a weakened market where:

• Momentum is slowing down (RSI and Aroon).
• Price levels are relatively low (below the SMA).

The strategy appears to be designed to capture potential rebounds in a mildly bearish or consolidating market, rather than markets experiencing extreme volatility or steep declines.

Suggestions for Improvement or Consideration

1. Additional Confirmation: Consider adding another indicator to confirm the rebound, such as an RSI divergence or a moving average crossover.
2. Exit Plan: Ensure the exit conditions are aligned with this logic to avoid holding positions if the market continues to fall.
3. Optimization: Ensure that values like 46.0 for RSI_4 and 0.942 for SMA_16 are optimized for your target market and time frame through proper backtesting.

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Condition #41 - Quick mode (Long)

Analysis of the Entry Signal

1. df["RSI_14"] < 36.0

   • Meaning: The 14-period Relative Strength Index (RSI) is below 36.
   • Interpretation: This indicates that the market is approaching oversold territory. While not yet at extreme levels (e.g., <30), it suggests weakening bullish momentum and the potential for a reversal.

2. df["AROONU_14"] < 25.0

   • Meaning: The 14-period Aroon Up is below 25.
   • Interpretation: This confirms weak bullish momentum, indicating that new highs are not being made and that the market may be trending downward or consolidating.

3. df["AROOND_14"] > 75.0

   • Meaning: The 14-period Aroon Down is above 75.
   • Interpretation: This indicates strong bearish momentum, as recent lows dominate the price action. Together with the Aroon Up condition, this reflects a significant bearish trend.

4. df["EMA_9"] < (df["EMA_26"] * 0.960)

   • Meaning: The 9-period Exponential Moving Average (EMA) is below 96% of the 26-period EMA.
   • Interpretation: This reflects short-term price weakness relative to a longer-term average, confirming bearish sentiment and adding further evidence of oversold conditions.

Overall Strategy Analysis

This signal aims to identify long entry opportunities in strongly bearish or oversold market conditions by leveraging:

• Momentum Indicators: RSI and Aroon provide confirmation of bearish trends and potential oversold states.
• Trend Dynamics: The EMA comparison ensures that the short-term trend is significantly weaker than the longer-term trend, supporting the idea of undervaluation.

Strengths

1. Momentum and Trend Convergence: The combination of RSI, Aroon, and EMA conditions ensures that momentum and trend signals align to indicate a bearish environment.
2. Confirmation of Oversold Conditions: The RSI and Aroon indicators effectively highlight potential oversold areas for a reversal or mean reversion.
3. Dynamic Trend Comparison: Using EMA multipliers provides a flexible way to detect significant deviations in short-term vs. long-term trends.

Suggestions for Improvement or Consideration

1. Refinement of RSI Threshold: Test different RSI levels (e.g., <30) to capture deeper oversold conditions, especially in strong bearish trends.
2. Volume Filter: Add a condition based on volume (e.g., higher-than-average volume) to confirm participation during potential reversal points.
3. Trend Context: Introduce a higher timeframe trend filter (e.g., EMA 50 > EMA 200) to avoid taking long trades during strong bearish markets.
4. Dynamic Multipliers: Consider adapting the 0.960 multiplier for the EMA condition based on recent volatility (e.g., ATR).
5. Exit Strategy: Define clear exit rules, such as targeting the 9-period EMA or another dynamic level for mean reversion, alongside stop-loss thresholds to limit risk.
6. Backtesting: Optimize the Aroon thresholds and EMA multipliers for specific assets or market conditions to ensure effectiveness.

Conclusion

This signal effectively combines momentum and trend-based criteria to identify potential reversal or undervaluation scenarios in a bearish market context. While robust in design, integrating additional filters such as volume and higher timeframe trends could improve its precision and profitability.

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Condition #42 - Quick mode (Long)

Analysis of the Entry Signal

1. df["WILLR_14"] < -50.0

   • Meaning: The 14-period Williams %R is below -50.
   • Interpretation: This indicates that the asset is in the lower half of its trading range, suggesting bearish momentum. However, it is not yet oversold, as the oversold threshold typically lies below -80.

2. df["STOCHRSIk_14_14_3_3"] < 20.0

   • Meaning: The Stochastic RSI (%K) with parameters 14_14_3_3 is below 20.
   • Interpretation: This signals an oversold condition on a short-term momentum indicator, suggesting the potential for a reversal.

3. df["WILLR_84_1h"] < -70.0

   • Meaning: The 84-period Williams %R on the 1-hour timeframe is below -70.
   • Interpretation: This indicates bearish momentum on a higher timeframe, supporting the bearish sentiment in the shorter-term signals.

4. df["STOCHRSIk_14_14_3_3_1h"] < 20.0

   • Meaning: The Stochastic RSI (%K) with parameters 14_14_3_3 on the 1-hour timeframe is below 20.
   • Interpretation: This confirms an oversold condition on the 1-hour timeframe, aligning with the shorter-term signals and increasing the likelihood of a reversal.

5. df["BBB_20_2.0_1h"] > 16.0

   • Meaning: The Bollinger Bandwidth (BBB) on the 1-hour timeframe with parameters 20_2.0 is greater than 16.
   • Interpretation: This indicates high volatility in the 1-hour timeframe. Elevated Bollinger Bandwidth often precedes significant price movements, making this condition a potential filter for trading opportunities.

6. df["close_max_48"] >= (df["close"] * 1.10)

   • Meaning: The highest close price in the last 48 periods is at least 10% higher than the current close price.
   • Interpretation: This highlights a significant downward movement over recent periods, suggesting that the asset is undervalued and potentially due for a mean reversion or bounce.

Overall Strategy Analysis

This signal aims to identify oversold conditions across multiple timeframes and high volatility environments with the potential for reversals. It combines momentum indicators, higher-timeframe confirmation, and volatility filters to enhance the precision of entry points.

Strengths

1. Multi-Timeframe Confirmation: The use of both current and 1-hour timeframe indicators ensures alignment of momentum signals, increasing reliability.
2. Volatility Awareness: The Bollinger Bandwidth condition ensures that trades are only considered in high-volatility environments, where significant price moves are more likely.
3. Momentum and Undervaluation: The combination of Williams %R, Stochastic RSI, and recent close price conditions effectively identifies oversold and undervalued scenarios.

Suggestions for Improvement or Consideration

1. Threshold Optimization:

   • Adjust -50.0 for Williams %R to a more oversold level (e.g., < -80.0) to filter out weaker signals.
   • Validate the 20.0 threshold for Stochastic RSI across different assets and market conditions.

2. Additional Filters:

   • Add a volume filter to confirm market participation in the reversal.
   • Introduce a higher timeframe trend filter (e.g., EMA 50 > EMA 200 on the daily timeframe) to ensure trades align with broader market trends.

3. Exit Strategy:

   • Clearly define exit conditions, such as targeting the mid or upper Bollinger Band or a specific RSI level.
   • Consider using trailing stops to lock in gains during potential reversals.

4. Dynamic Conditions:

   • Use an adaptive Bollinger Bandwidth threshold (e.g., based on recent historical averages) to account for varying market volatility.
   • Adjust the 1.10 multiplier for recent close prices dynamically, depending on the asset's typical volatility.

5. Backtesting and Validation:

   • Thoroughly test this setup on multiple asset classes and timeframes to ensure robustness.
   • Evaluate signal effectiveness in different market conditions (e.g., trending vs. ranging).

Conclusion

This signal leverages a well-rounded combination of multi-timeframe momentum, volatility, and undervaluation criteria to pinpoint potential reversal opportunities. While effective in concept, further refinement of thresholds and the addition of volume or trend-based filters could improve its performance.

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Condition #43 - Quick mode (Long)

Analysis of the Entry Signal

1. df["RSI_14"] < 40.0

   • Meaning: The 14-period RSI is below 40.
   • Interpretation: Indicates bearish momentum but not yet oversold (typically below 30). This suggests weakness in price but leaves room for further downward movement.

2. df["MFI_14"] < 40.0

   • Meaning: The 14-period Money Flow Index is below 40.
   • Interpretation: Combines price and volume to indicate bearish sentiment. Values below 50 show weakness, and levels below 20 are oversold. This condition aligns with the RSI to confirm bearish pressure.

3. df["AROONU_14"] < 25.0

   • Meaning: The 14-period Aroon Up indicator is below 25.
   • Interpretation: Indicates that recent highs are distant, signaling a lack of bullish momentum.

4. df["EMA_26"] > df["EMA_12"]

   • Meaning: The 26-period EMA is above the 12-period EMA.
   • Interpretation: Suggests that the short-term trend is below the longer-term trend, confirming bearish market conditions.

5. (df["EMA_26"] - df["EMA_12"]) > (df["open"] * 0.024)

   • Meaning: The difference between the 26-period EMA and the 12-period EMA is greater than 2.4% of the current opening price.
   • Interpretation: Ensures a significant bearish divergence between the two EMAs, reinforcing a strong bearish trend.

6. (df["EMA_26"].shift() - df["EMA_12"].shift()) > (df["open"] / 100.0)

   • Meaning: The previous period's EMA difference is greater than 1% of the current opening price.
   • Interpretation: Confirms that the bearish divergence between the EMAs is consistent over time, ensuring trend persistence.

7. df["close"] < (df["EMA_20"] * 0.958)

   • Meaning: The current closing price is below 95.8% of the 20-period EMA.
   • Interpretation: Indicates that the price is significantly below its short-term average, suggesting bearish momentum.

8. df["close"] < (df["BBL_20_2.0"] * 0.992)

   • Meaning: The current closing price is below 99.2% of the 20-period lower Bollinger Band.
   • Interpretation: Indicates that the price is near or outside the lower Bollinger Band, suggesting oversold conditions and potential for a reversal.

Overall Strategy Analysis

This entry signal identifies oversold conditions within a strong bearish trend. It combines momentum indicators (RSI, MFI, Aroon Up), EMA relationships to confirm trend strength, and Bollinger Bands to detect extreme price levels.

Strengths

1. Momentum and Volume Confirmation: The RSI and MFI together provide a robust view of price and volume-based momentum.
2. Trend Reinforcement: The EMA conditions ensure that trades align with a strong bearish trend.
3. Oversold Levels: The Bollinger Band condition ensures that entries are made at potentially oversold price levels, increasing the chance of a reversal.

Suggestions for Improvement or Consideration

1. Optimize Thresholds:

   • Adjust the RSI and MFI levels (e.g., < 30.0) to focus on more extreme oversold conditions.
   • Experiment with the Bollinger Band multiplier (e.g., 0.990 instead of 0.992) for tighter entry points.

2. Trend Continuation Filter:

   • Add a higher timeframe trend filter to ensure the trade aligns with broader market direction, reducing false signals.

3. Volume Confirmation:

   • Introduce a volume condition (e.g., df["volume"] > df["volume"].rolling(20).mean()) to confirm market participation.

4. Exit Strategy:

   • Define clear exit conditions, such as reaching the mid or upper Bollinger Band, or when the RSI exceeds 50.
   • Use a trailing stop to protect gains during potential reversals.

5. Backtesting and Optimization:

   • Test the setup on multiple assets and timeframes to determine its robustness.
   • Validate the 2.4% EMA divergence condition against historical data for varying market environments.

Conclusion

This signal effectively combines momentum, trend, and volatility conditions to pinpoint potential oversold scenarios within bearish trends. While it appears well-constructed, refining thresholds and adding additional filters could further improve its precision and reliability.

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Condition #101 - Rapid mode (Long)

Analysis of the Entry Signal

1. df["STOCHRSIk_14_14_3_3"] < 20.0

• Meaning: The 14-period Stochastic RSI (k line) is below 20.
• Interpretation: This condition identifies oversold momentum based on the Stochastic RSI, a momentum oscillator that combines the RSI and Stochastic indicators. A value below 20 suggests that the price is in an oversold state and could reverse upwards soon.

2. df["WILLR_14"] < -80.0

• Meaning: The 14-period Williams %R is below -80.
• Interpretation: Williams %R measures overbought or oversold levels. A value below -80 indicates an oversold condition, signaling potential upward reversal. Combining this with Stochastic RSI strengthens the oversold momentum signal.

3. df["AROONU_14"] < 25.0

• Meaning: The 14-period Aroon Up indicator is below 25.
• Interpretation: Indicates that recent highs are distant, reflecting a lack of bullish momentum. This condition adds a trend-following element to the logic, ensuring that the market is not already in a strong uptrend, which might reduce the effectiveness of a reversal signal.

4. df["close"] < (df["EMA_20"] * 0.978)

• Meaning: The current closing price is below 97.8% of the 20-period EMA.
• Interpretation: This confirms that the price is trading significantly below its short-term average, indicating a bearish bias and reinforcing the oversold narrative. By setting this condition, the model ensures entries at a lower price relative to the average, enhancing the risk-reward ratio for potential reversals.

Overall Strategy Analysis

This entry logic is designed to detect oversold conditions with potential for upward reversals in a bearish or consolidating market. It combines momentum oscillators (Stochastic RSI, Williams %R) with a trend indicator (Aroon Up) and a price-position condition (EMA deviation).

Strengths

1. Robust Oversold Confirmation: The combination of Stochastic RSI and Williams %R provides a strong indication of oversold conditions, increasing confidence in potential reversals.
2. Trend Awareness: Aroon Up ensures that the model only signals entries when the market shows weak bullish momentum, aligning with the reversal context.
3. Price Context: The EMA-based condition ensures trades occur at a discount relative to the short-term trend, enhancing entry timing.

Suggestions for Improvement or Consideration

1. Add Volume Analysis:
   • Introduce a volume filter, such as df["volume"] > df["volume"].rolling(20).mean(), to confirm market participation during potential reversals.
2. Optimize Thresholds:
   • Experiment with tighter thresholds (e.g., df["STOCHRSIk_14_14_3_3"] < 15.0) to identify more extreme oversold conditions.
3. Incorporate Volatility Metrics:
   • Add Bollinger Band conditions (e.g., df["close"] < df["BBL_20_2.0"]) to identify when prices reach extreme deviations.
4. Higher Timeframe Confirmation:
   • Add a higher timeframe filter, such as a 50-period EMA trend alignment, to ensure the reversal aligns with broader market sentiment.
5. Exit Strategy:
   • Define clear exit rules, such as:
     • Stochastic RSI crossing above 80.
     • Price closing above the 20-period EMA.
     • A percentage-based trailing stop to lock in gains during potential upward trends.
6. Backtesting and Refinement:
   • Test this strategy across different assets and timeframes to evaluate its robustness.
   • Optimize the 0.978 EMA multiplier for varying market conditions to improve precision.

Conclusion

This logic effectively identifies oversold opportunities within a bearish or weak market trend. The combination of momentum indicators, trend awareness, and price-position context offers a well-rounded entry strategy. However, introducing additional filters (e.g., volume, volatility) and refining thresholds could enhance reliability and profitability.

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Condition 120 - Grind mode (Long)

Analysis of the Entry Signal

1. df["STOCHRSIk_14_14_3_3"] < 20.0

   • Meaning: The stochastic RSI (with a 14-period RSI, smoothed over 14, 3, and 3 periods) is below 20.
   • Interpretation: This indicates that the asset is in a deeply oversold state on a stochastic basis, which combines RSI and stochastic oscillators. Such low values suggest the market may be near a reversal or rebound point.

2. df["WILLR_14"] < -80.0

   • Meaning: The 14-period Williams %R is below -80.
   • Interpretation: Williams %R values below -80 indicate that the market is in oversold territory, reinforcing the idea that the asset might be due for a rebound.

3. df["AROONU_14"] < 25.0

   • Meaning: The 14-period Aroon Up is below 25.
   • Interpretation: A low Aroon Up value indicates a lack of new highs in the recent period, suggesting weakness in upward momentum. This complements the oversold signals by adding a momentum perspective.

4. df["close"] < (df["EMA_20"] * 0.978)

   • Meaning: The closing price is below 97.8% of the 20-period exponential moving average.
   • Interpretation: This signals that the price is trading significantly below its recent average, suggesting short-term weakness or oversold conditions.

Overall Strategy Analysis

This signal logic aims to identify long entry points in deeply oversold market conditions, using a combination of:

• Oscillator-Based Oversold Levels: Both stochastic RSI and Williams %R provide strong indicators of oversold conditions, increasing the likelihood of capturing reversal points.
• Momentum Weakness: The Aroon indicator ensures that the market has lost upward momentum.
• Price Deviation: The EMA condition adds a layer of confirmation by requiring the price to be significantly below its recent average, indicating potential undervaluation.

Strengths

1. Diverse Indicator Use: The strategy combines oscillators (Stochastic RSI, Williams %R), momentum (Aroon), and price action (EMA deviation) for a well-rounded approach.
2. High Oversold Confirmation: Multiple indicators pointing to oversold conditions reduce the risk of false signals.

Suggestions for Improvement or Consideration

1. Volume Confirmation: Add a condition for volume, such as high volume during the oversold state, to confirm the market’s interest in a potential reversal.
2. Reversal Signal: Consider including a trigger for a reversal confirmation (e.g., a bullish candlestick pattern or a moving average crossover).
3. Exit Strategy Alignment: Ensure that the exit logic complements the entry signal by defining clear profit targets or using mean reversion thresholds.
4. Backtesting and Optimization: Test and optimize thresholds like 20.0 for Stochastic RSI, -80.0 for Williams %R, and 0.978 for EMA multipliers for the target asset and time frame.

This signal is well-designed for capturing potential rebounds in oversold markets.

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Condition #143 - Top Coins mode (Long)

Analysis of the Entry Signal

1. df["RSI_3"] < 40.0

   • Meaning: The 3-period RSI is below 40.
   • Interpretation: A very short-term momentum indicator suggests the price is in bearish momentum but not yet oversold (typically below 30). This indicates temporary weakness in the market.

2. df["STOCHRSIk_14_14_3_3"] < 50.0

   • Meaning: The %K line of the 14-period Stochastic RSI is below 50.
   • Interpretation: Indicates bearish sentiment within the Stochastic RSI's range. Values below 50 suggest momentum is leaning downward.

3. df["EMA_26"] > df["EMA_12"]

   • Meaning: The 26-period EMA is greater than the 12-period EMA.
   • Interpretation: Confirms that the short-term trend (12 EMA) is below the longer-term trend (26 EMA), indicating bearish conditions.

4. (df["EMA_26"] - df["EMA_12"]) > (df["open"] * 0.020)

   • Meaning: The gap between the 26 EMA and the 12 EMA is greater than 2% of the current opening price.
   • Interpretation: Reinforces a strong bearish divergence between the EMAs, suggesting that the market is trending downward with sufficient momentum.

5. (df["EMA_26"].shift() - df["EMA_12"].shift()) > (df["open"] / 100.0)

   • Meaning: The difference between the 26 EMA and 12 EMA from the previous period is greater than 1% of the current opening price.
   • Interpretation: Confirms that the bearish divergence between the EMAs has been consistent across time, strengthening the reliability of the trend.

Overall Strategy Analysis

This entry signal focuses on capturing bearish momentum within a strong downward trend, combining very short-term indicators (RSI 3) with longer-term EMA relationships and the Stochastic RSI. The conditions aim to align with bearish market sentiment while identifying moments of temporary weakness.

Strengths

1. Momentum and Trend Confirmation:

   • The combination of short-term RSI and Stochastic RSI with EMA relationships ensures trades align with both short-term momentum and longer-term trend direction.

2. Trend Strength Filter:

   • The EMA gap conditions (both current and historical) ensure that only strong bearish trends are considered, reducing the likelihood of false entries during sideways markets.

3. Short-Term Focus:

   • Using the 3-period RSI allows the strategy to react quickly to short-term market changes while maintaining alignment with the overall trend.

Suggestions for Improvement or Consideration

1. Optimize Thresholds:

   • Test the RSI 3 threshold at < 30.0 to focus on more extreme oversold conditions for potentially better entry timing.
   • For the Stochastic RSI, a threshold of < 20.0 could identify more precise moments of oversold momentum.

2. Volume Confirmation:

   • Add a condition for volume to ensure that entries occur during periods of significant market activity (e.g., df["volume"] > df["volume"].rolling(20).mean()).

3. Higher Timeframe Confirmation:

   • Include a condition to confirm the trend on a higher timeframe (e.g., the 1-hour chart) to filter out false signals in choppy markets.

4. Exit Strategy:

   • Define clear exit rules, such as:
   • RSI 3 exceeds 70.
   • Price crosses above the 12 EMA.
   • Stochastic RSI %K exceeds 80.

5. Backtesting:

   • Test the signal on different assets and timeframes to evaluate its effectiveness across varying market conditions.

Conclusion

This signal is designed to identify bearish momentum in strong downward trends, using both short-term and longer-term confirmations. While it is well-constructed, refining the thresholds and adding volume or higher timeframe filters could improve its robustness and reduce noise.

Condition #501 - Normal mode (Short)

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Condition #502 - Normal mode (Short)

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Condition #503 - Normal mode (Short)

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Condition #504 - Normal mode (Short)

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Condition #542 - Quick mode (Short)

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Condition #543 - Rapid mode (Short)

TODO

Condition #641 - Top Coins mode (Short)

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Condition #642 - Top Coins mode (Short)

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Position adjustment modes

adjust_trade_position function

The adjust_trade_position function in the NostalgiaForInfinityX5 strategy of Freqtrade is responsible for dynamically adjusting the position of an ongoing trade based on specific conditions. Here's a breakdown of its functionality:

Overview

This method is invoked to determine whether and how a trade's position should be adjusted, such as increasing the stake or modifying the strategy, depending on the trade's status, market conditions, and predefined rules. It supports both long and short positions and handles different trading modes like rebuy and grind modes.

Key Components

1. Position Adjustment Toggle

if self.position_adjustment_enable == False:
    return None

• Purpose: If position_adjustment_enable is set to False, the function exits without making any changes.
• Effect: This provides a global switch to enable or disable position adjustments.

2. Enter Tag Parsing

enter_tag = "empty"
if hasattr(trade, "enter_tag") and trade.enter_tag is not None:
    enter_tag = trade.enter_tag
enter_tags = enter_tag.split()

• Purpose: The enter_tag is extracted from the trade object to determine the mode or strategy associated with the trade (e.g., rebuy, grind).
• Effect: Tags are split into a list for easier matching against predefined mode-specific tags.

3. Rebuy Mode

if not trade.is_short and (
    all(c in self.long_rebuy_mode_tags for c in enter_tags)
    or (
        any(c in self.long_rebuy_mode_tags for c in enter_tags)
        and all(c in (self.long_rebuy_mode_tags + self.long_grind_mode_tags) for c in enter_tags)
    )
):
    return self.long_rebuy_adjust_trade_position(...)

• Purpose: If the trade is a long position and matches specific long_rebuy_mode_tags, it triggers the long_rebuy_adjust_trade_position method.
• Effect: Adjusts the trade by implementing the logic for the rebuy mode, which likely increases the position size to capitalize on favorable market conditions.

4. Grinding Mode (Long Trades)

elif not trade.is_short and (
    any(c in (...) for c in enter_tags)
    or not any(c in (...) for c in enter_tags)
):
    return self.long_grind_adjust_trade_position(...)

• Purpose: If the trade is a long position and matches tags related to grinding modes (e.g., normal, pump, rapid), it triggers the long_grind_adjust_trade_position method.
• Effect: Implements adjustments for grinding mode strategies, which might aim to optimize profits in slow-moving markets.

5. Grinding Mode (Short Trades)

elif trade.is_short and (
    any(c in (...) for c in enter_tags)
    or not any(c in (...) for c in enter_tags)
):
    return self.short_grind_adjust_trade_position(...)

• Purpose: Similar to the grinding mode for long trades, but applied to short trades. Uses predefined tags like short_normal_mode_tags and others.
• Effect: Adjusts short positions based on grinding mode rules.

6. Default Return

return None

• Purpose: If none of the above conditions are met, no adjustment is made.
• Effect: Ensures the function doesn't make unnecessary changes when conditions don't align.

Additional Insights

• Customizability: The function relies heavily on predefined tags (long_rebuy_mode_tags, long_grind_mode_tags, etc.), allowing for extensive customization of trading behavior.
• Reusability: Calls to long_rebuy_adjust_trade_position, long_grind_adjust_trade_position, and short_grind_adjust_trade_position encapsulate specific logic for those modes, making the function modular.
• Flexibility: Supports both long and short trades and distinguishes between various trading modes for nuanced control over adjustments.

High-Level Purpose

The adjust_trade_position function is a critical part of the strategy's risk management and profit optimization framework. It dynamically adjusts ongoing trades based on the context, enabling the strategy to respond adaptively to changing market conditions. This can help maximize profits in favorable situations (e.g., rebuying) and mitigate risks in less favorable ones.

Grind mode

The "grind mode" is a strategy which involves increasing the position in a losing trade. It activates when the asset's price falls significantly below the initial entry point, offering an opportunity to buy more of the asset at a lower price and thereby reduce the average entry price.

Objective of "Grind Mode":

The goal of "grind mode" is to improve the chances of making a profit when the asset's price eventually reverses upward. By buying more at a lower price, the overall loss of the trade is reduced, requiring a smaller upward movement to reach the breakeven point.

Key Parameters of "Grind Mode":

• grind_mode_stake_multiplier_spot and grind_mode_stake_multiplier_futures: These parameters determine the multiplier applied to the position size for each additional buy during "grind mode." For example, a value of [0.20, 0.30, 0.40, 0.50] means the first additional buy will be 20% of the initial position size, the second 30%, and so on.

• grind_mode_first_entry_profit_threshold_spot and grind_mode_first_entry_profit_threshold_futures: These parameters define the profit threshold that must be reached to exit the first entry in "grind mode" for spot and futures markets, respectively.

• grind_mode_first_entry_stop_threshold_spot and grind_mode_first_entry_stop_threshold_futures: These parameters define the maximum loss threshold (stop-loss) that will be applied to the first entry in "grind mode" for spot and futures markets, respectively.

• grind_mode_max_slots: Defines the maximum number of positions (slots) that can be opened in "grind mode" for the same trading pair.

• grind_mode_coins: List of cryptocurrencies for which "grind mode" will be activated.

In summary, "grind mode" in NostalgiaForInfinityX5 is an aggressive strategy aimed at leveraging price drops to accumulate a position at a lower average cost. By increasing the position during market weakness, "grind mode" seeks to maximize potential profits when the price reverses.

Explanation of Grinding Levels: GD1 (Grinding Level 1)

Grinding Level 1 (GD1) is the first and foundational level of the grinding logic. This level is designed to rebalance and optimize a trade's position when specific market conditions are met. Here's a detailed breakdown of how GD1 operates:

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1. Conditions for GD1 Activation

GD1 is triggered based on several factors:

• Profitability Thresholds: The function checks if the trade's profitability is within a specific range.
  • For example, if the distance ratio (difference between current price and the last entry price) falls below the threshold defined for GD1, a rebuy is considered.
• Market Timing:
  • No recent trades: Ensures that enough time has passed since the last entry or exit to avoid overtrading.
  • A significant drop in price (slice_profit < -0.02): Indicates a potential opportunity to rebuy at a lower price.
• Mode Dependency:
  • It may activate in grind mode, de-risk mode, or recovery mode, depending on the strategy’s state.

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2. Actions Taken in GD1

GD1 allows for both buying and selling, depending on market conditions.

Buy Action

• Purpose: Increase the trade size when prices drop to take advantage of lower prices.
• Buy Amount Calculation:
  • Proportional to the base trade size (slice_amount), multiplied by a GD1-specific stake multiplier.
  • Adjusted for leverage if the trade is in futures mode.
  • Ensures the buy amount is greater than the minimum stake and doesn’t exceed the maximum allowed stake.
• Market Indicators for Entry:
  • The function evaluates indicators like RSI, Aroon, and moving averages to confirm entry opportunities.
• Logging and Tagging: Creates logs and tags the order as gd1 for tracking and analysis.

Sell Action

• Purpose: Exit part of the position if profitability targets are reached.
• Sell Amount Calculation:
  • Based on the total amount held at GD1 and adjusted for leverage.
  • Ensures the remaining stake does not fall below the minimum required size.
• Profit Thresholds:
  • The function uses thresholds specific to GD1 to determine when it is profitable to sell.
  • These thresholds include fees (fee_open_rate, fee_close_rate) to ensure net profitability.
• Logging and Tagging: Logs the action and tags the sell order as gd1.

---

3. Differences Specific to GD1

• Entry Level Thresholds:
  • GD1 has the most lenient thresholds for triggering rebuys compared to higher levels (GD2–GD6).
  • This is because GD1 aims to capitalize on the earliest opportunities during a grinding phase.
• Multiplier: The stake multiplier for GD1 is specific to this level and ensures controlled risk during the initial rebuy.
• Sub-Grinds: GD1 supports multiple sub-grinds, meaning it can trigger additional entries within its scope until the maximum sub-grind count is reached.

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4. Risk Mitigation in GD1

• Minimum Stake Enforcement: Ensures that the position size does not drop below a manageable level.
• Exit Prioritization: If profitability is uncertain, GD1 prioritizes exiting the position to avoid unnecessary risk.

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Summary

GD1 is the initial step in the grinding strategy, focusing on controlled risk, early opportunities, and incremental adjustments to the position. It sets the stage for higher levels (GD2–GD6), which progressively tighten thresholds and adjust stake sizes for more refined rebalancing.

Explanation of Grinding Levels: GD2 (Grinding Level 2)

Grinding Level 2 (GD2) builds upon the logic of GD1, introducing slightly stricter conditions and refinements for adjusting trade positions. GD2 operates as the second layer in the grinding strategy, progressively optimizing the trade by addressing market conditions that require additional rebalancing or profit-taking.

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1. Conditions for GD2 Activation

GD2 is triggered under the following circumstances:

• Distance Ratio and Profit Thresholds:
  • The price distance from the last entry (distance ratio) must fall below the predefined threshold specific to GD2.
  • GD2 thresholds are generally more conservative than GD1, reflecting the strategy's aim to reduce risk as the position evolves.
• Timing Constraints:
  • A minimum time gap since the last entry or exit is enforced (e.g., at least 10 minutes).
  • For market corrections or significant drops (slice_profit < -0.02), GD2 considers opportunities to rebuy.
• Mode Sensitivity:
  • GD2 can operate in grind mode, de-risk mode, or recovery mode, depending on the trade's context.
  • It is more selective in de-risking compared to GD1.

---

2. Actions Taken in GD2

GD2 supports both buying and selling actions, similar to GD1, but with distinct thresholds and calculations.

Buy Action

• Purpose: Add to the position when the market moves in a favorable direction, within the constraints of GD2-specific thresholds.
• Buy Amount Calculation:
  • Uses the base slice amount (slice_amount) multiplied by the GD2 stake multiplier.
  • Adjusted for leverage in futures mode.
  • Ensures the buy amount is:
    • Greater than the minimum stake.
    • Less than the maximum allowable stake for the trade.
• Market Indicators for Entry:
  • GD2 may apply stricter conditions for indicators like RSI, Aroon, or moving averages.
  • These ensure that the rebuy aligns with refined market signals compared to GD1.
• Logging and Tagging: Logs the buy action with a gd2 tag for tracking purposes.

Sell Action

• Purpose: Exit part of the position when profit targets for GD2 are achieved.
• Sell Amount Calculation:
  • Based on the total position size at GD2 and adjusted for leverage.
  • Ensures that the remaining position size does not fall below the minimum stake threshold.
• Profit Thresholds:
  • GD2 uses tighter profit thresholds than GD1, accounting for both entry and exit fees (fee_open_rate and fee_close_rate).
  • Only executes a sell if the net profit exceeds the required threshold.
• Logging and Tagging: Logs the action and tags the sell order as gd2.

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3. Differences Specific to GD2

• Stricter Thresholds:
  • GD2 introduces more stringent distance ratio and profitability requirements compared to GD1.
  • This reflects the need for greater precision in later grinding stages.
• Lower Multiplier: The GD2 stake multiplier may be smaller than GD1, reflecting reduced risk tolerance as the grinding process progresses.
• Sub-Grind Opportunities: Similar to GD1, GD2 supports multiple sub-grinds, enabling additional entries within the GD2 level until the maximum sub-grind count is reached.

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4. Risk Mitigation in GD2

• Progressive De-Risking: GD2 focuses more on de-risking than GD1, especially if market conditions show prolonged unfavorable trends.
• Minimum Stake Enforcement: Ensures that any adjustment leaves the trade above the minimum manageable position size.
• Exit Over Rebuy: If profit thresholds are uncertain, GD2 prioritizes selling over buying to avoid overexposure.

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5. GD2 as a Transition Point

GD2 acts as a transition phase in the grinding strategy:

• It refines the trade by responding to more precise market movements.
• Sets the stage for the higher grinding levels (GD3–GD6), which implement even stricter thresholds and risk management rules.

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Summary

GD2 introduces enhanced selectivity, risk reduction, and refined thresholds compared to GD1. It ensures that grinding continues in a controlled manner, balancing opportunities for profit with risk management.

Explanation of Grinding Levels: GD3 (Grinding Level 3)

Grinding Level 3 (GD3) is the third tier in the grinding strategy. At this level, the conditions become even more restrictive compared to GD2, emphasizing risk mitigation and ensuring that trades align closely with the strategy's profitability and safety objectives.

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1. Conditions for GD3 Activation

GD3 triggers when:

• Distance Ratio Thresholds:
  • The price distance from the last entry (distance ratio) must fall below the stricter GD3-specific threshold.
  • This threshold ensures that entries or adjustments are only made when the market signals strong alignment with the strategy.
• Timing Constraints:
  • GD3 enforces slightly longer time gaps compared to GD2 between successive entries or exits (e.g., minimum 10 minutes since the last entry and 2 hours for significant market corrections).
• Market Signals:
  • The market must show consistent alignment with grind-specific indicators (e.g., RSI, Aroon, EMA).
  • A significant price drop (slice_profit < -0.03) or other predefined conditions must justify adjustments.
• Mode Sensitivity:
  • GD3 operates selectively in grind mode, de-risk mode, or recovery mode, depending on the trade's context and broader market conditions.

---

2. Actions Taken in GD3

Like the previous levels, GD3 allows for buying and selling actions but with tighter controls.

Buy Action

• Purpose: Add to the position if market conditions meet the stricter GD3 criteria.
• Buy Amount Calculation:
  • Based on the base slice amount (slice_amount) multiplied by the GD3-specific stake multiplier.
  • Adjusted for leverage in futures trading.
  • Ensures the buy amount:
    • Exceeds the minimum stake requirement.
    • Does not surpass the maximum allowable stake for the trade.
• Market Indicators for Entry:
  • GD3 relies on more robust market signals, such as:
    • Lower RSI thresholds: Indicating oversold conditions.
    • Aroon Indicator: Suggesting trend exhaustion.
    • EMA Levels: Confirming strong support levels.
• Logging and Tagging: Tags the order as gd3 for tracking and analysis.

Sell Action

• Purpose: Exit part of the position if profitability thresholds specific to GD3 are reached.
• Sell Amount Calculation:
  • Determined based on the GD3 position size and adjusted for leverage.
  • Ensures the remaining position does not drop below the minimum stake threshold.
• Profit Thresholds:
  • GD3 uses more conservative profit thresholds than GD2 to reflect the level’s increased focus on risk reduction.
  • Accounts for fees (fee_open_rate and fee_close_rate) to ensure net profitability.
• Logging and Tagging: Logs the sell action and tags the order as gd3.

---

3. Differences Specific to GD3

• Stricter Profitability Criteria:
  • GD3 requires higher confidence in profitability before allowing buys or sells.
• Lower Multiplier: The GD3 stake multiplier is smaller than GD2, reflecting the need for tighter risk control at this stage.
• Sub-Grinds: GD3 permits additional entries within its scope, but the maximum sub-grind count for GD3 is typically lower than GD1 or GD2.

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4. Risk Mitigation in GD3

• Selective Entry: Only executes trades under strong market alignment, reducing unnecessary exposure.
• Focus on Safety: De-risking takes priority over aggressive profit-seeking.
• Minimum Stake Enforcement: Ensures that the position size remains above the minimum manageable level.

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5. GD3 as a Refinement Stage

GD3 is a refinement stage in the grinding strategy:

• It acts as a filter, ensuring that only high-probability opportunities are acted upon.
• Sets the groundwork for the more restrictive grinding levels (GD4–GD6), which focus heavily on risk mitigation and controlled position adjustments.

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Summary

GD3 is designed for controlled rebalancing, selective adjustments, and risk mitigation. It introduces stricter thresholds and prioritizes safety over profit-seeking, ensuring that the strategy remains resilient during volatile market conditions.

Explanation of Grinding Levels: GD4 (Grinding Level 4)

Grinding Level 4 (GD4) introduces even stricter criteria than GD3, focusing heavily on risk management and precise adjustments. GD4 is part of the advanced stages of grinding, aimed at carefully optimizing trades while minimizing exposure to adverse market conditions.

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1. Conditions for GD4 Activation

GD4 is triggered under specific, highly selective conditions:

• Distance Ratio Thresholds:
  • The price distance from the last entry (distance ratio) must meet the stricter GD4-specific thresholds.
  • GD4’s thresholds are designed to ensure that only the most favorable market opportunities are acted upon.
• Timing Requirements:
  • Longer time gaps between trades compared to GD3 (e.g., 2+ hours since the last significant adjustment).
  • Ensures sufficient time for market patterns to develop before taking further action.
• Market Signals:
  • Strong alignment with grind-specific indicators is required.
  • Additional indicators, such as higher timeframe moving averages or trend strength, may be considered to confirm the market's direction.
• Mode Sensitivity:
  • GD4 operates in grind mode, de-risk mode, or recovery mode, but with a primary focus on de-risking the trade.

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2. Actions Taken in GD4

GD4 enables both buying and selling actions, but these are even more selective than in GD3.

Buy Action

• Purpose: Add to the position in scenarios where the market provides highly favorable conditions.
• Buy Amount Calculation:
  • The base slice amount (slice_amount) is multiplied by the GD4-specific stake multiplier.
  • Adjusted for leverage if the trade is in futures mode.
  • Buy amount:
    • Must exceed the minimum stake requirement.
    • Cannot exceed the maximum allowable stake for the trade.
• Market Indicators for Entry:
  • RSI: Oversold conditions must be confirmed, with additional constraints like divergence with price action.
  • EMA Alignment: Prices must be near key support levels defined by exponential moving averages.
  • Volume and Volatility Analysis: Lower volatility or high volume spikes may be required to trigger a GD4 entry.
• Logging and Tagging: Logs the buy action with a gd4 tag for tracking and analysis.

Sell Action

• Purpose: Exit part of the position when profitability thresholds for GD4 are reached.
• Sell Amount Calculation:
  • Based on the total GD4-specific position size and adjusted for leverage.
  • Ensures the remaining stake does not drop below the minimum stake threshold.
• Profit Thresholds:
  • GD4 has stricter profit thresholds than GD3, ensuring only highly profitable exits are executed.
  • Fees (fee_open_rate, fee_close_rate) are accounted for to ensure net profitability.
• Logging and Tagging: Logs the sell action and tags the order as gd4.

---

3. Differences Specific to GD4

• Tighter Risk Controls:
  • GD4 prioritizes safety and only takes actions with high confidence.
  • Position adjustments are smaller and more controlled compared to GD1–GD3.
• Selective Entry Criteria:
  • GD4’s conditions require multiple market indicators to align, significantly reducing the frequency of adjustments.
• Fewer Sub-Grinds: The maximum sub-grind count for GD4 is lower than for GD1–GD3, reflecting its focus on precision over quantity.

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4. Risk Mitigation in GD4

• De-Risking Priority: GD4 emphasizes reducing exposure when conditions are less favorable, even at the expense of lower profitability.
• Minimum Stake Enforcement: Maintains a manageable position size to prevent overexposure.
• Controlled Adjustments: Ensures that any rebuy or sell action aligns with both profitability and risk reduction goals.

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5. GD4 as a Precision Stage

GD4 represents a fine-tuning stage in the grinding strategy:

• It avoids unnecessary adjustments unless there is strong evidence of market alignment.
• GD4 sets the groundwork for GD5 and GD6, which adopt even more conservative approaches.

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Summary

GD4 is a precision-driven grinding level that balances the need for profitability with an increased emphasis on risk management. Its stricter conditions ensure that trades are adjusted only under optimal circumstances, reflecting the advanced stage of the grinding process.

Explanation of Grinding Levels: GD5 (Grinding Level 5)

Grinding Level 5 (GD5) is the penultimate level in the grinding strategy. At this stage, the conditions are extremely restrictive, prioritizing risk minimization and ensuring that any adjustments to the trade are highly selective. GD5 focuses heavily on profit preservation and strategic de-risking, making it a critical stage in the advanced grinding process.

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1. Conditions for GD5 Activation

GD5 activation depends on highly specific and cautious conditions:

• Distance Ratio Thresholds:
  • The price distance from the last entry must meet even stricter thresholds than GD4.
  • These thresholds are designed to only allow adjustments in exceptional scenarios that align with the strategy's overall objectives.
• Timing Requirements:
  • GD5 enforces longer cooldown periods between trades compared to GD4 (e.g., several hours or more since the last adjustment).
  • This ensures the market has had sufficient time to stabilize or confirm a trend.
• Market Signals:
  • Requires strong and clear alignment with grind-specific indicators, such as:
    • RSI levels indicating extreme oversold or overbought conditions.
    • EMA and volume trends that confirm robust support or resistance levels.
  • Additional confirmations from higher timeframe indicators may also be required.
• Mode Sensitivity:
  • GD5 primarily operates in de-risk mode or recovery mode, with grind mode playing a secondary role.
  • The focus is on preserving capital and carefully managing exposure.

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2. Actions Taken in GD5

GD5 permits buying and selling actions, but with even greater caution and precision than GD4.

Buy Action

• Purpose: Add to the position in rare scenarios where the market provides exceptional opportunities.
• Buy Amount Calculation:
  • The base slice amount (slice_amount) is multiplied by the GD5-specific stake multiplier.
  • Adjusted for leverage in futures mode.
  • The calculated buy amount:
    • Must exceed the minimum stake threshold.
    • Must remain well below the maximum allowable stake, ensuring no excessive exposure.
• Market Indicators for Entry:
  • GD5 evaluates a combination of advanced market signals:
    • RSI Divergence: Strong divergence between price action and RSI is required.
    • EMA Alignment: The price must be near or bouncing off critical long-term EMA levels.
    • Volatility Analysis: Low volatility conditions combined with clear directional trends.
• Logging and Tagging: The action is tagged as gd5 for tracking and analysis.

Sell Action

• Purpose: Exit part or all of the position when GD5 profit thresholds are reached, prioritizing risk reduction.
• Sell Amount Calculation:
  • Determined based on the GD5-specific position size, adjusted for leverage.
  • Ensures the remaining stake does not drop below the minimum threshold.
• Profit Thresholds:
  • GD5 has stricter profit thresholds than GD4, ensuring only significant gains trigger exits.
  • Fees (fee_open_rate and fee_close_rate) are included to guarantee net profitability.
• Logging and Tagging: Logs the sell action and tags the order as gd5.

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3. Differences Specific to GD5

• Extreme Selectivity:
  • GD5 has the most restrictive entry conditions among the grinding levels so far.
  • Adjustments are rare and only occur in highly favorable scenarios.
• Smaller Adjustments:
  • The GD5 stake multiplier is significantly lower than earlier levels, reflecting the need for minimal risk at this stage.
• Few Sub-Grinds:
  • GD5 supports only a minimal number of sub-grinds, ensuring limited re-entry opportunities.

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4. Risk Mitigation in GD5

• De-Risking Priority:
  • GD5 focuses heavily on reducing exposure, especially in uncertain or volatile markets.
  • De-risking takes precedence over seeking new opportunities.
• Capital Preservation:
  • Ensures that the strategy minimizes potential losses while securing profits from earlier grinding levels.
• Minimum Stake Enforcement:
  • Maintains a manageable position size, preventing over-leveraging or overexposure.

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5. GD5 as a Capital Preservation Stage

GD5 represents the penultimate step in the grinding strategy:

• It prioritizes precision and risk aversion, ensuring only the best opportunities are acted upon.
• It serves as a preparatory phase for GD6, which adopts the strictest conditions for trade adjustments.

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Summary

GD5 is a highly selective and risk-averse stage in the grinding process, emphasizing capital preservation, strategic de-risking, and precision adjustments. Its strict thresholds and conservative approach ensure that trades remain resilient in volatile market conditions.

Explanation of Grinding Levels: GD6 (Grinding Level 6)

Grinding Level 6 (GD6) is the final and most conservative level in the grinding strategy. It is designed to make adjustments only under the most extreme and favorable conditions, with an emphasis on capital protection, de-risking, and closing out positions if profitability targets or safety thresholds are met. GD6 serves as the ultimate safety net in the grinding process.

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1. Conditions for GD6 Activation

GD6 activation occurs only when:

• Distance Ratio Thresholds:
  • The price distance from the last entry must satisfy the most restrictive threshold in the grinding strategy.
  • These thresholds are calibrated to ensure GD6 operates only in exceptional scenarios.
• Timing Constraints:
  • GD6 enforces the longest cooldown periods between trades, often requiring multiple hours or significant market movements since the last adjustment.
• Market Signals:
  • Requires the strongest confirmations from grind-specific and broader market indicators, including:
    • RSI, Aroon, EMA, and volume trends.
    • Potential confluence with higher timeframe support/resistance levels.
    • Indicators of market exhaustion or reversal.
• Mode Sensitivity:
  • GD6 is heavily oriented towards de-risk mode and recovery mode, with grind mode having a minimal role.
  • The focus is on exiting the position safely rather than seeking additional opportunities.

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2. Actions Taken in GD6

GD6 supports both buying and selling, but its operations are highly restrictive and focused on capital preservation.

Buy Action

• Purpose: Make minimal additions to the position only if market conditions are extraordinarily favorable.
• Buy Amount Calculation:
  • Uses the base slice amount (slice_amount) multiplied by the GD6-specific stake multiplier, which is the smallest across all grinding levels.
  • Adjusted for leverage in futures mode.
  • Ensures the buy amount:
    • Exceeds the minimum stake requirement.
    • Remains well below the maximum allowable stake, maintaining a conservative position.
• Market Indicators for Entry:
  • Requires extreme oversold conditions or other significant indicators of market reversal.
  • Indicators must align across multiple timeframes to ensure robust entry signals.
• Logging and Tagging: Tags the action as gd6 for tracking and analysis.

Sell Action

• Purpose: Exit the position entirely or partially when GD6 profit thresholds or safety conditions are met.
• Sell Amount Calculation:
  • Calculated based on the total GD6-specific position size and adjusted for leverage.
  • Ensures that any remaining stake meets the minimum threshold if the position is not entirely closed.
• Profit Thresholds:
  • GD6 has the strictest profit thresholds, requiring significant gains before executing a sell.
  • Fees (fee_open_rate and fee_close_rate) are accounted for, ensuring only net profitability triggers an exit.
• Logging and Tagging: Logs the action and tags the order as gd6.

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3. Differences Specific to GD6

• Maximum Selectivity:
  • GD6 operates with the most restrictive conditions, making adjustments only in scenarios of extreme confidence.
• Minimal Adjustments:
  • The GD6 stake multiplier is the smallest, reflecting the need for absolute risk control.
• Single Sub-Grind: GD6 typically allows only one or very few sub-grinds, minimizing re-entry opportunities.

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4. Risk Mitigation in GD6

• De-Risking as Priority:
  • GD6 focuses almost exclusively on exiting positions safely, reducing exposure to market volatility.
• Capital Preservation:
  • Ensures that any adjustments prioritize protecting the trader's capital rather than chasing incremental gains.
• Minimum Stake Enforcement:
  • Maintains a manageable position size, even for the final grinding level.

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5. GD6 as a Final Safety Stage

GD6 represents the last line of defense in the grinding strategy:

• It avoids aggressive adjustments, focusing solely on safe exits or minimal re-entries in highly favorable conditions.
• It serves as the strategy's "endgame," ensuring trades are closed with maximum safety and profitability.

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Summary

GD6 is the most conservative and risk-averse stage in the grinding strategy. It focuses on capital protection, strategic exits, and minimal adjustments to ensure the safety of the trader's capital. This level is used sparingly and only in the most exceptional market conditions.

Summary of Grinding Levels (GD1–GD6)

The Grinding Levels (GD1–GD6) in the strategy represent a structured approach to dynamically adjusting trade positions based on market conditions. Each level progressively tightens its conditions and priorities, transitioning from opportunistic profit-seeking (GD1) to maximum capital preservation (GD6).

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GD1: Initial Opportunity Level

• Purpose:
  • Take advantage of early opportunities in a grinding phase.
• Key Features:
  • Lenient thresholds for rebuys and adjustments.
  • Larger stake multipliers to capitalize on early favorable market conditions.
  • Higher frequency of adjustments.
• Focus: Growth-oriented, prioritizing profit opportunities.

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GD2: Refinement Level

• Purpose:
  • Refine trade adjustments by introducing tighter thresholds.
• Key Features:
  • Slightly stricter distance ratios and profitability requirements.
  • Reduced stake multipliers to manage risk more cautiously.
  • Focus on selective opportunities compared to GD1.
• Focus: Balanced between profit-seeking and risk management.

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GD3: Controlled Risk Level

• Purpose:
  • Tighten control over trade adjustments as the grinding phase matures.
• Key Features:
  • Strict entry and exit criteria.
  • Further reduced stake multipliers and limited sub-grinds.
  • Emphasis on aligning adjustments with robust market signals (e.g., RSI, EMA).
• Focus: Shift toward controlled risk and selective profit-taking.

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GD4: Precision Level

• Purpose:
  • Implement precise adjustments under highly favorable market conditions.
• Key Features:
  • Even stricter thresholds for profitability and distance ratios.
  • Minimal stake adjustments and fewer sub-grinds allowed.
  • Heavy reliance on higher timeframe confirmations and confluence.
• Focus: Prioritize capital safety with occasional precision adjustments.

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GD5: Advanced Risk Control

• Purpose:
  • Focus on risk minimization and capital preservation.
• Key Features:
  • Extremely conservative thresholds and smaller stake multipliers.
  • Rare adjustments, limited to scenarios of exceptional market alignment.
  • De-risking takes precedence over profit-seeking.
• Focus: Reduce exposure while preserving existing gains.

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GD6: Final Safety Stage

• Purpose:
  • Provide a last line of defense for the trade, focusing on safe exits.
• Key Features:
  • The strictest conditions and the smallest stake multipliers.
  • Minimal adjustments; primarily used for closing out positions.
  • Heavy focus on capital preservation and avoiding overexposure.
• Focus: Strategic exits and maximum safety.

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Key Progression Across Levels

• Risk Tolerance: Decreases progressively from GD1 to GD6.
• Profit Thresholds: Tighten at each level, requiring greater confidence in profitability.
• Stake Multipliers: Reduce with each level to limit risk exposure.
• Frequency of Adjustments: Drops significantly, with GD6 allowing the fewest adjustments.
• Focus Transition:
  • GD1–GD3: Balance between profit-seeking and risk management.
  • GD4–GD6: Heavy emphasis on risk control and capital preservation.

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How GD Levels Work Together

• Layered Strategy: The grinding levels create a multi-layered approach that adapts to market conditions dynamically.
• Early Aggression, Later Precision: Early levels (GD1–GD3) exploit opportunities, while later levels (GD4–GD6) focus on safety and strategic exits.
• Flexible Adjustments: Each level responds to market changes based on predefined thresholds, ensuring the strategy remains adaptive.

Rebuy Mode

The "rebuy mode" is another type of strategy that, like "grind mode," aims to increase the position in a losing trade. However, unlike "grind mode," which activates during significant price drops, "rebuy mode" is triggered when the price falls below specific thresholds, which are less severe than those for "grind mode."

Objective of "Rebuy Mode":

The goal of "rebuy mode" is to reduce the average entry price of a trade by buying more of the asset at a lower price. This decreases the overall loss and increases the chances of making a profit when the price recovers.

Key Parameters of "Rebuy Mode":

• rebuy_mode_stake_multiplier: This parameter defines the percentage of the initial position size to be used for each additional buy during "rebuy mode." For example, a value of 0.2 means each additional buy will be 20% of the original position size.

• rebuy_mode_max: Defines the maximum number of additional buys (rebuys) that can be made in a single trade.

• rebuy_mode_derisk_spot and rebuy_mode_derisk_futures: These parameters set the loss thresholds to activate the "derisk" in "rebuy mode" for spot and futures markets, respectively.

• rebuy_mode_stakes_spot and rebuy_mode_stakes_futures: These parameters are a list of multipliers applied to the position size for each additional buy (rebuy) in spot and futures markets, respectively.

• rebuy_mode_thresholds_spot and rebuy_mode_thresholds_futures: These parameters define the loss thresholds that must be reached to trigger each additional buy (rebuy) in spot and futures markets, respectively.

In summary, "rebuy mode" in NostalgiaForInfinityX5 is a strategy designed to capitalize on small price drops to improve the position of a trade. By making additional buys at lower prices, "rebuy mode" reduces the average cost of the position, increasing the likelihood of achieving profitability when the price eventually recovers.

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Derisk mode

The "derisk" is a feature within the NostalgiaForInfinityX5 trading strategy designed to reduce the risk of a trade. It activates when the asset's price moves against the open position, whether it is a long (buy) or short (sell) position.

The goal of "derisk" is to mitigate potential losses or secure a small profit by selling a portion of the position. This is done at a price considered favorable, typically a point where the loss is minimized or even a small profit is achieved.

Key Parameters of "Derisk"

• Derisk Thresholds: These thresholds define the point at which the "derisk" is triggered for different operation modes (regular, rebuy, grind mode) and market types (spot or futures). Examples include:

  • regular_mode_derisk_spot and regular_mode_derisk_futures: Thresholds for "derisk" in regular mode for spot and futures markets, respectively.
  • rebuy_mode_derisk_futures: Threshold for "derisk" in rebuy mode for futures markets.

• Calculation of the Amount to Sell: The algorithm determines the amount of the asset to sell during the "derisk" process. This decision is based on factors such as the current profit, the total position size, and the minimum order size.

In summary, the "derisk" in NostalgiaForInfinityX5 functions as a safety mechanism to protect trades from significant losses. By selling a portion of the position at strategic moments, the "derisk" aims to minimize the impact of unfavorable price movements.

Exit signals

Tags

Long exit signals (original)

Sell signal 1 (exit_{mode_name}_1_X_Y}

The Sell Signal 1 tags focus on overbought conditions identified by a high RSI (>84) and consistent price action above the upper Bollinger Band for the last 5 candles. These tags aim to secure profits in extended bullish trends, with exits further categorized based on whether the price is above or below the EMA 200. This differentiation helps adapt the strategy to the broader trend, allowing profit-taking in both strong uptrends and potential bearish reversals.

exit_{mode_name}_1_1_1

Triggered when the closing price of the last candle is above the EMA 200. This indicates an overall bullish trend, but the trade is closed due to overbought conditions (high RSI and price above the Bollinger Bands). It represents a profit-taking decision in a strong uptrend.

exit_{mode_name}_1_2_1:

Triggered when the closing price of the last candle is below the EMA 200. This suggests a weaker trend or a bearish shift, combined with overbought conditions. The exit is more cautious, aiming to secure profits before further potential downside.

Sell signal 2 (exit_{mode_name}_2_X_Y}

The Sell Signal 2 tags respond to stronger overbought conditions, with an RSI threshold of >86 and price action above the upper Bollinger Band for the last 3 candles. These tags aim for quicker profit-taking compared to Sell Signal 1, targeting trades where the market shows extreme overbought signals but over a shorter timeframe. The exits are further categorized based on whether the price is above or below the EMA 200, adjusting the strategy to the prevailing trend while locking in profits.

exit_{mode_name}_2_1_1:

Triggered when the closing price of the last candle is above the EMA 200. Indicates a bullish trend, but the trade is closed due to even more extreme overbought conditions (higher RSI threshold of >86 and price above the Bollinger Bands for the last 3 candles). The exit reflects a strategy to secure profits in a strong uptrend before a likely correction.

exit_{mode_name}_2_2_1:

Triggered when the closing price of the last candle is below the EMA 200. Suggests a weaker or bearish trend while still closing the trade due to extreme overbought conditions. This exit aims to lock in profits in a less favorable trend environment before potential further downside.

Exit signal 3 (exit_{mode_name}_3_X_X)

This sell signal focuses solely on an extremely high RSI threshold (>88) as the primary trigger, making it the most sensitive to overbought conditions among the three signals. It does not depend on Bollinger Band conditions like the previous signals, allowing for a quicker response.

exit_{mode_name}_3_1_1:

Triggered when the closing price of the last candle is above the EMA 200. Indicates a bullish trend, but the trade is exited due to extremely overbought conditions (RSI > 88). This exit represents a precautionary profit-taking decision in an overheated market during a strong uptrend.

exit_{mode_name}_3_2_1:

Triggered when the closing price of the last candle is below the EMA 200. Suggests a weaker or bearish trend while still exiting the trade due to extremely overbought conditions. The goal is to secure profits in a less favorable trend environment before a likely price correction.

Sell Signal 4 (exit_{mode_name}_4_X_X):

The Sell Signal 4 tags consider overbought conditions on multiple timeframes, with RSI thresholds of >84 on the current timeframe and >80 on the 1-hour timeframe. This combination ensures that both the short-term and medium-term market conditions are overheated. The exits are categorized based on whether the price is above or below the EMA 200, allowing the strategy to adapt to the overall trend while securing profits in highly overbought scenarios.

Sell Signal 4 introduces the additional confirmation of multi-timeframe RSI conditions, making it more robust in identifying overbought scenarios compared to previous signals. This ensures the exit is based on a broader market perspective, increasing its reliability in highly volatile markets.

exit_{mode_name}_4_1_1

• Triggered when:
  • The closing price of the last candle is above the EMA 200, indicating a bullish trend.
  • RSI > 84 on the current timeframe and RSI > 80 on the 1-hour timeframe, signaling overbought conditions across multiple timeframes.
  • The trade has a current profit >1%.
• This exit reflects a cautious profit-taking strategy in a strong uptrend, driven by overbought signals confirmed on both short-term and medium-term timeframes.

exit_{mode_name}_4_2_1

• Triggered when:
  • The closing price of the last candle is below the EMA 200, indicating a weaker or bearish trend.
  • RSI > 84 on the current timeframe and RSI > 80 on the 1-hour timeframe, signaling overbought conditions across multiple timeframes.
  • The trade has a current profit >1%.
• This exit secures profits in a less favorable trend environment, taking into account that overbought conditions may lead to stronger corrections when the overall trend is bearish.

Sell Signal 6 (exit_{mode_name}_6_X_Y):

The Sell Signal 6 tag identifies overbought conditions in a transitional trend phase, where the price is below the long-term EMA 200 (suggesting a bearish trend) but above the short-term EMA 50 (indicating a temporary bullish recovery). With RSI > 79, the signal targets profit-taking before a potential reversal. This exit prioritizes locking in profits in uncertain or transitional market conditions.

Sell Signal 6 is designed for scenarios where the market is transitioning between bearish and bullish phases. The combination of price action relative to EMA 200 and EMA 50, along with overbought RSI conditions, ensures that profits are secured before the potential continuation of the bearish trend. This signal is particularly useful for avoiding losses in markets showing temporary recoveries within a long-term downtrend.

exit_{mode_name}_6_1

• Triggered when:
  • The closing price of the last candle is below the EMA 200, signaling a bearish overall trend.
  • The closing price is above the EMA 50, indicating a short-term bullish recovery within a broader bearish trend.
  • RSI > 79, which suggests overbought conditions.
  • The trade has a current profit >1%.
• This exit reflects a cautious approach, aiming to secure profits in a potentially unstable market where short-term bullish momentum may not sustain due to the broader bearish context.

Sell Signal 8 (exit_{mode_name}_8_X_Y)

The Sell Signal 8 tags target scenarios where the price has significantly exceeded the upper Bollinger Band (BBU) on the 1-hour timeframe, specifically by more than 14%. This indicates extreme overbought conditions and a potential reversal. The exits are further categorized based on whether the price is above or below the EMA 200, adapting the strategy to the overall trend while locking in profits in overheated markets.

Sell Signal 8 focuses on extreme price deviations relative to Bollinger Bands on a higher timeframe (1-hour). By requiring the price to exceed the BBU by 14%, it ensures that exits are only triggered under truly overextended market conditions. The differentiation based on EMA 200 further refines the strategy, allowing it to adapt to both bullish and bearish trends while prioritizing profit-taking.

exit_{mode_name}_8_1_1

• Triggered when:
  • The closing price is above the upper Bollinger Band (BBU) on the 1-hour timeframe by 14% or more, indicating extreme overbought conditions.
  • The closing price is above the EMA 200, suggesting an overall bullish trend.
  • The trade has a current profit >1%.
• This exit aims to secure profits in a strong uptrend where the price has reached unsustainable levels relative to its volatility.

exit_{mode_name}_8_2_1

• Triggered when:
  • The closing price is above the upper Bollinger Band (BBU) on the 1-hour timeframe by 14% or more, indicating extreme overbought conditions.
  • The closing price is below the EMA 200, signaling a weaker or bearish trend.
  • The trade has a current profit >1%.
• This exit reflects a more cautious approach, aiming to secure profits in a potentially bearish market where extreme overbought conditions are unlikely to sustain.

Long exit signals (main)

Sell with profits over SMA 200

exit_{mode_name}_o_X

The exit_{mode_name}_o_X signals are designed to manage trade exits based on a combination of profit targets and Relative Strength Index (RSI) values. This mechanism is particularly focused on locking in profits when the trade is in a generally bullish context, as determined by the closing price being above the EMA 200.

1. Profit Thresholds (X):

   elif current_profit >= 0.001:

   • The logic evaluates different profit levels ranging from 0.1% (0.001) to above 20% (0.2).
   • For each profit range, an RSI-based condition determines whether the exit signal is triggered.
   • The profit ranges are as follows:
     • 0.001 to 0.01 (1%): Exit if RSI < 10.0.
     • 0.01 to 0.02 (2%): Exit if RSI < 28.0.
     • 0.02 to 0.03 (3%): Exit if RSI < 30.0.
     • (Continues incrementally with higher thresholds up to 20% or more).

2. RSI Thresholds:

   if last_candle["RSI_14"] < threshold:

   • RSI thresholds are dynamic and increase with profit levels, reflecting a willingness to hold the trade longer in stronger bullish conditions.
   • For higher profits, the strategy allows higher RSI thresholds, signaling that a pullback from overbought conditions (indicated by a lower RSI) is acceptable.

3. Key Characteristics of the Signal

   • Dynamic Profit-Taking:

     • As profits increase, the RSI threshold becomes more forgiving, allowing trades to ride the bullish trend longer. This ensures profits are locked in while still capturing potential upside momentum.

   • Adaptation to Market Strength:

     • By only considering trades where the price is above the EMA 200, the strategy avoids reactive exits in weaker markets and focuses on maximizing gains in a strong market.

   • Granular Exit Management:

     • The use of incremental profit brackets (e.g., 1% to 2%, 2% to 3%) allows the strategy to handle trade exits with fine-tuned precision, adapting to varying market conditions.

4. Potential Improvements

   1. RSI Divergences:

      • Incorporate divergence detection (e.g., RSI showing bearish divergence despite profits) to enhance the robustness of exits.

   2. Volatility Adjustments:

      • Adjust profit or RSI thresholds dynamically based on market volatility to better adapt to fast-moving markets.

   3. Trailing Stop Integration:

      • Introduce trailing stop-loss mechanisms for profits beyond 10% to lock in gains while leaving room for potential upside.

Sell with profits under SMA 200

exit_{mode_name}_u_X

The exit_{mode_name}_u_X signals are designed to handle trade exits when the market is in a bearish context, as identified by the closing price being below the EMA 200. These signals focus on managing trades that are profitable or near breakeven but where the market shows signs of weakness or exhaustion.

• Protective Profit-Taking:

  • The strategy aims to secure small profits or minimize potential losses in a bearish market, where the risk of further declines is higher.

• Dynamic Adjustments:

  • The RSI thresholds increase with profit levels, allowing trades with higher gains to tolerate more fluctuation before exiting.

• Risk-Averse in Downtrends:

  • By only triggering when the price is below the EMA 200, the logic ensures tighter risk management in bearish trends.

exit_{mode_name}_u_0:

• Triggered for minimal profits (0.1%–1%).
• RSI < 12.0 indicates extreme bearish momentum, prompting a cautious exit.

exit_{mode_name}_u_1 to exit_{mode_name}_u_10:

• Covers profit ranges from 1% to 12%.
• RSI thresholds gradually increase from 30.0 to 48.0, reflecting a more relaxed approach as profits grow.

exit_{mode_name}_u_11:

• Profit range: 12%–20%.
• RSI threshold decreases slightly to 46.0, emphasizing the need to protect profits in this range.

exit_{mode_name}_u_12:

• Triggered for profits of 20% or more.
• RSI < 44.0 ensures the trade is exited promptly if bearish conditions persist, protecting significant gains.

Strengths

1. Bearish Trend Adaptation:

   • The strategy recognizes the increased risks in a downtrend and prioritizes protecting profits or minimizing exposure.

2. Granular Exit Control:

   • Fine-tuned profit brackets allow for precise control of trade exits, making the strategy adaptable to various market conditions.

3. RSI-Based Validation:

   • The use of RSI ensures that exits are triggered based on both market momentum and the trade's profitability.

Potential Improvements

1. Multi-Timeframe Confirmation:

   • Incorporate higher timeframe RSI or trend indicators to validate bearish conditions further.

2. Dynamic Volatility Adjustment:

   • Adjust profit and RSI thresholds based on market volatility to better handle fast-moving or stagnant markets.

3. Divergence Signals:

   • Integrate bearish RSI divergence detection to enhance the signal’s robustness in identifying weakening trends.

Downtrend/descending based sells

exit_{mode_name}_d_{X}_{Y}

The two digits in the label d_X_Y have specific meanings related to the strategy's decision-making process:

1. First Digit (X):

   • Represents the profit range for the trade.
   • Example:
     • X = 0: Profit range between 0.001 and 0.01 (low profits).
     • X = 1: Profit range between 0.01 and 0.02 (moderate profits).
     • Higher values would likely correspond to higher profit ranges.

2. Second Digit (Y):

   • Indicates the specific condition that triggered the exit within the given profit range.
   • Each number corresponds to a unique combination of technical indicators:
     • Example conditions:
       • Overbought signals (e.g., RSI, Williams %R).
       • Momentum loss (ROC).
       • Pressure indicators like CMF or AROON.
     • Y = 1 to Y = 7: Different technical setups or combinations that meet the strategy's criteria for exiting.

Summary

• The first digit (X) defines the profit range.
• The second digit (Y) specifies the exact technical condition that justifies the exit within that range.

Stoploss signals

Stoploss doom (exit_{mode_name}_stoploss_doom):

The Stoploss Doom Signal targets scenarios where a trade has reached a catastrophic loss level and no longer meets the conditions for remaining open. This signal is designed to act as a safeguard, ensuring that trades are exited promptly in extreme adverse conditions to prevent further losses.

• Triggered when:
  • The profit loss exceeds the "doom" threshold (adjusted for market type and leverage).
  • By default stop_threshold_doom_spot is 25% and stop_threshold_doom_futures is 60%.
  • The entry conditions are no longer valid, indicating the trade setup is no longer favorable.
  • The trade is recent (post-September 13, 2024) or being analyzed in a backtesting environment.
• This exit prevents further losses and resets the trade for a better opportunity.

TODO tags

• exit_{mode_name}_stoploss_u_e
• exit_profit_{mode_name}_max
• exit_long_quick_q_{1-10}
• exit_long_rapid_rpd_{1-10}
• exit_long_grind_g
• exit_{mode_name}o{1-10} # exit with price over SMA 200, and current_profit over {1-10} and last candle under RSI_14 specific level.
• exit_{mode_name}w{0-12}_{0-16}

long_exit_normal

The long_exit_normal function is a method that determines whether a trade should close its position under certain predefined conditions. Its logic is structured around multiple checks that include profitability, indicators, trends, and stop-loss mechanisms. Here's a breakdown of its key components:

1. Initial Condition: Skip for Non-Profitable Trades

• The function begins by checking if the initial profit ratio (profit_init_ratio) is greater than 0.0. If the trade is already at a loss, most subsequent checks are skipped.

2. Sequential Exit Signals

The function iterates through several exit signal functions in a specific order. It stops as soon as one of them returns a True signal:

1. long_exit_signals:

   • Used for basic or original exit signals based on specific criteria.

2. long_exit_main:

   • The main logic for detecting whether the trade should exit, likely using more refined or frequently checked conditions.

3. long_exit_williams_r:

   • Uses the Williams %R indicator for determining whether to exit. This indicator detects overbought or oversold conditions.

4. long_exit_dec:

   • Detects downtrends or descending patterns to trigger an exit.

3. Stop-Losses

• If no exit signals were triggered, the function checks the stop-loss conditions using long_exit_stoploss.
• This method ensures the trade is exited if the losses reach predefined thresholds, preserving capital.

4. Target Profit Mechanism

• The function integrates a target profit mechanism using self.target_profit_cache.
• It evaluates whether the pair has reached or exceeded its target profit. If yes, it triggers an exit and adjusts targets dynamically for better performance.

5. Maximizing Profit Targets

• For trades already marked with a stop-loss reason, the function attempts to recover if the profit increases by a small margin (e.g., 0.005).
• When possible, it dynamically raises the profit target to ensure maximum returns.

6. Fallback and Signal Consolidation

• If no conditions are met but the profit_init_ratio exceeds 0.005, the function updates the target profit with a "maximization" signal.
• Finally, it ensures certain conditions are met before returning an exit signal (True) or skipping (False).

Return Values

• True, signal_name: Indicates the trade should exit based on a specific signal.
• False, None: No exit conditions were met; the trade remains open.

Key Observations

1. Flexible Structure: The function is modular, allowing easy addition or modification of exit conditions.
2. Profit Optimization: The logic favors maximizing profit before closing a trade, showing a preference for efficiency over immediate closure.
3. Dynamic Adjustments: By relying on self.target_profit_cache, the function dynamically tracks and updates profit targets for optimal results.
4. Risk Management: The inclusion of stop-loss mechanisms ensures the function adheres to solid risk management principles.

long_exit_signals

The long_exit_signals function identifies whether specific market conditions are met to exit a long position. It uses a set of predefined technical indicators and thresholds to determine whether the position should be closed. Below is a detailed breakdown of its logic:

Function Parameters

• mode_name: A string representing the mode of operation (likely used to distinguish between different strategies or signals).
• current_profit, max_profit, max_loss: Metrics representing the trade's profitability and loss.
• last_candle and previous_candles: Contain data from recent candles (OHLC, indicators like RSI, EMA, Bollinger Bands, etc.).
• trade: Represents the trade being evaluated.
• current_time: Current timestamp of evaluation.
• buy_tag: Likely a tag or label related to the trade entry conditions.

Exit Conditions

The function sequentially evaluates various sell signals. If one signal triggers, the function exits early with True and a descriptive exit tag. Otherwise, it returns False and None.

1. Sell Signal 1: RSI and Bollinger Band Exceedance Over 5 Candles

• Conditions:
  • RSI (RSI_14) > 84.0.
  • Current and previous 5 candles' closing prices are above the Bollinger Band upper boundary (BBU_20_2.0).
• Additional Checks:
  • If the close price > EMA 200, it uses the tag exit_{mode_name}_1_1_1.
  • If close price ≤ EMA 200, it uses exit_{mode_name}_1_2_1.
  • Requires current_profit > 0.01 (ensures a minimum profit).

Implication: This signal indicates overbought conditions sustained over multiple candles, suggesting a potential reversal.

2. Sell Signal 2: RSI and Bollinger Band Exceedance Over 3 Candles

• Conditions:
  • RSI > 86.0.
  • Current and previous 3 candles' closing prices are above the Bollinger Band upper boundary.
• Tagging:
  • Similar structure to Signal 1, with tags adjusted to exit_{mode_name}_2_1_1 and exit_{mode_name}_2_2_1.

Implication: A variation of Signal 1 with tighter conditions and a higher RSI threshold, targeting quicker exits.

3. Sell Signal 3: RSI Alone

• Condition: RSI > 88.0.
• Tagging:
  • Close price > EMA 200: exit_{mode_name}_3_1_1.
  • Otherwise: exit_{mode_name}_3_2_1.

Implication: A simple threshold-based exit for extreme overbought conditions.

4. Sell Signal 4: RSI with 1-Hour RSI Confirmation

• Conditions:
  • RSI > 84.0.
  • 1-hour RSI (RSI_14_1h) > 80.0.
• Tagging:
  • Similar to previous signals, with tags exit_{mode_name}_4_1_1 or exit_{mode_name}_4_2_1.

Implication: Confirms overbought conditions across multiple timeframes, strengthening the exit signal.

5. Sell Signal 6: EMA and RSI Combined

• Conditions:
  • Close price < EMA 200 and > EMA 50.
  • RSI > 79.0.
• Tagging: Always exits with exit_{mode_name}_6_1.

Implication: Indicates weakening upward momentum and potential for a reversal.

6. Sell Signal 8: Extreme Deviation from 1-Hour Bollinger Band

• Condition: Current close price > 1-hour Bollinger Band upper boundary (BBU_20_2.0_1h) * 1.14.
• Tagging:
  • Close price > EMA 200: exit_{mode_name}_8_1_1.
  • Otherwise: exit_{mode_name}_8_2_1.

Implication: Suggests unsustainable price deviations and potential reversion to the mean.

Excluded Signals

• Signal 7 is commented out. It involves a crossover with EMA 12/26 and RSI conditions but is not active in the logic.

Return Value

• True, exit_tag: An exit condition was triggered.
• False, None: No conditions were met; the trade remains open.

Key Observations

1. Prioritization: Signals are evaluated sequentially, meaning earlier conditions take precedence.
2. Focus on Overbought Conditions: The function heavily relies on RSI and Bollinger Bands to detect overbought scenarios.
3. Dynamic Tagging: Exit tags are descriptive, allowing detailed tracking of which signal triggered the exit.
4. Profit Threshold: Ensures exits occur only when there is a minimum profit (> 0.01), avoiding premature closures.

Suggestions for Improvement

1. Scalability: Consider modularizing signal conditions into separate functions for easier maintenance and testing.
2. Parameterization: Use configuration files or parameters for thresholds like RSI levels (e.g., 84.0, 88.0), allowing flexibility without code changes.
3. Optimize Priority: Review the order of signals to ensure the most critical ones are evaluated first, reducing unnecessary computations.

long_exit_main function

This function evaluates whether to exit a long position based on a set of conditions related to the current profit, RSI levels, and the relationship between the closing price and the EMA 200 indicator.

Strengths:

1. Granular Exit Levels:

   • The function provides finely detailed exit conditions depending on the current_profit. This allows for a highly adaptable strategy based on profit thresholds.

2. Dynamic RSI Adjustments:

   • The RSI thresholds decrease as profits increase, which aligns well with the logic of securing profits when overbought signals are detected.

3. EMA 200 Condition:

   • The function considers the relationship between the price and the EMA 200, differentiating between bullish (close > EMA_200) and bearish (close < EMA_200) contexts, which is a solid approach for managing trades in varying market conditions.

4. Clear Tagging:

   • The mode_name and suffixes like _o_ or _u_ (indicating above or below EMA 200) allow for easy tracking and debugging of exit signals.

Weaknesses and Suggestions for Improvement:

1. Repetitive Code:

   • There is a lot of duplicated code between the close > EMA_200 and close < EMA_200 blocks. Both segments perform similar checks with slightly adjusted RSI thresholds.
   • Solution: Extract the repetitive logic into a helper function that accepts parameters such as current_profit, RSI_thresholds, and a tag suffix. This will improve maintainability.

2. Static Thresholds:

   • Both the profit and RSI thresholds are hardcoded, which makes the function less adaptable to changes in market conditions or user preferences.
   • Solution: Use a configuration or parameterized approach to allow dynamic adjustments of these thresholds.

3. Lack of Comments:

   • The function lacks inline comments explaining the rationale behind specific thresholds (e.g., why RSI_14 < 42.0 for current_profit >= 0.2).
   • Solution: Add comments to clarify the logic for future developers or traders.

4. Limited Signal Types:

   • The function only considers RSI_14 and EMA_200. While these are commonly used, other indicators or conditions (e.g., volume, ATR, or MACD) might improve its accuracy.
   • Solution: Consider integrating additional indicators or conditions to create a more robust strategy.

5. Binary Outcome:

   • The function returns either a signal (True) or nothing (False). It does not account for scenarios where partial exits or staggered profit-taking might be optimal.
   • Solution: Modify the return logic to include a more nuanced response, such as suggested exit percentages.

6. Performance Considerations:

   • Checking many conditions sequentially for each candle could slow down performance, especially with high-frequency strategies or large datasets.
   • Solution: Optimize the order of checks or use vectorized operations if applicable in the broader context.

Code Refactor Suggestion:

Here's an example of how the function could be refactored:

def long_exit_main(
    self,
    mode_name: str,
    current_profit: float,
    last_candle,
    RSI_thresholds: dict,
    ema_threshold: float,
    suffix: str,
) -> tuple:
    for profit_range, rsi_limit in RSI_thresholds.items():
        lower_bound, upper_bound = profit_range
        if upper_bound > current_profit >= lower_bound and last_candle["RSI_14"] < rsi_limit:
            return True, f"exit_{mode_name}_{suffix}_{RSI_thresholds[profit_range]}"
    return False, None

# Example Usage:
RSI_THRESHOLDS_ABOVE_EMA = {
    (0.001, 0.01): 10.0,
    (0.01, 0.02): 28.0,
    (0.02, 0.03): 30.0,
    # Add other ranges here...
    (0.2, float("inf")): 42.0,
}

RSI_THRESHOLDS_BELOW_EMA = {
    (0.001, 0.01): 12.0,
    (0.01, 0.02): 30.0,
    (0.02, 0.03): 32.0,
    # Add other ranges here...
    (0.2, float("inf")): 44.0,
}

if last_candle["close"] > last_candle["EMA_200"]:
    return long_exit_main(
        self,
        mode_name,
        current_profit,
        last_candle,
        RSI_THRESHOLDS_ABOVE_EMA,
        last_candle["EMA_200"],
        "o"
    )
else:
    return long_exit_main(
        self,
        mode_name,
        current_profit,
        last_candle,
        RSI_THRESHOLDS_BELOW_EMA,
        last_candle["EMA_200"],
        "u"
    )

This approach eliminates redundancy and makes the function easier to maintain and extend.

long_exit_williams_r function

Analysis of the Function long_exit_williams_r

The long_exit_williams_r function is used to generate exit signals for long (buy) positions in an automated trading system. Its logic is based on a series of conditions involving technical indicators, primarily the Williams %R, to identify opportune moments to close a position and secure profits.

Below is a detailed breakdown of the function's logic:

Purpose

The function aims to identify overbought conditions or signs of market weakness that suggest the asset's price may begin to decline. In such cases, the function issues an exit signal to protect the accumulated profits from a long position.

Context

The long_exit_williams_r function is just one of many exit functions used within the trading system. It is called after other exit functions, such as long_exit_main, and only executes if the previous functions have not generated an exit signal. This indicates that long_exit_williams_r acts as an additional or backup exit mechanism.

Indicators

The function relies on various technical indicators, including:

• Williams %R (WILLR): A momentum indicator that measures overbought and oversold levels.
• Relative Strength Index (RSI): Another momentum indicator that evaluates the magnitude of recent price changes to assess overbought and oversold conditions.
• RSI across different timeframes (RSI_3_1h, RSI_3_4h): Used to provide a broader perspective on RSI trends.
• Aroon (AROONU_14_4h): An indicator that measures trend strength by comparing recent highs and lows.
• Rate of Change (ROC): A momentum indicator that calculates the rate of price change over a specific period.
• Chaikin Money Flow (CMF): A volume-based indicator measuring the flow of money into and out of an asset.

Conditional Logic

The long_exit_williams_r function employs a series of nested if statements to evaluate different combinations of indicators and threshold levels. Each condition is designed to capture a specific market scenario that may signal a potential reversal of the bullish trend. If a condition is met, the function returns True along with a unique signal name, indicating that an exit order should be executed.

Examples of Conditions

• If WILLR_480 is greater than -0.1, WILLR_14 is greater than or equal to -1.0, and RSI_14 is greater than 75.0, the function triggers the exit signal exit_{mode_name}_w_0_1.
• If RSI_3 is greater than 99.0, WILLR_14 is greater than -1.0, and both RSI_3_1h and RSI_3_4h are below 50.0, the function triggers the exit signal exit_{mode_name}_w_10_13.

Benefits

• Using a logic framework based on technical indicators can help automate exit decisions and minimize the emotional impact on trading.

Limitations

• It is important to recognize that no technical indicator is perfect. Indicators can generate false signals or miss exit opportunities.
• The configuration of the indicators and threshold levels must be carefully adjusted to suit specific market conditions and trading goals.

Summary

The long_exit_williams_r function uses a combination of technical indicators and complex conditional logic to generate exit signals for long positions. While this function can be a valuable tool for automated traders, it is crucial to understand its limitations and use it alongside other risk management strategies.

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long_exit_dec function

The long_exit_dec function is part of the source code for an automated trading strategy. This function is used to generate exit signals for long (buy) positions by employing a combination of technical indicators and complex conditional logic.

General Logic

The long_exit_dec function implements a series of nested conditions using if and elif statements. Each condition evaluates the current market state through technical indicators such as RSI, WILLR, STOCHRSIk, AROONU, ROC, and CMF. These conditions aim to identify moments when the asset's price may be about to reverse its upward trend, signaling a good opportunity to sell and secure profits.

Exit Signals

Each condition within the long_exit_dec function is associated with a specific exit signal, identified by a unique name starting with exit_{mode_name}_d_.

Examples of exit signals within long_exit_dec include:

• exit_{mode_name}_d_2_29: This signal triggers when RSI_3 is greater than 80.0, RSI_3_1h is less than 25.0, RSI_3_4h is less than 20.0, and STOCHRSIk_14_14_3_3_1h is greater than 30.0.
• exit_{mode_name}_d_10_16: This signal activates when RSI_3 is greater than 95.0, AROONU_14_4h is greater than 25.0, ROC_9_4h is less than -25.0, AROONU_14_1d is greater than 50.0, and change_pct_1d is less than -15.0.
• exit_{mode_name}_d_12_103: This signal triggers when RSI_3 is greater than 98.0, WILLR_14 is greater than -10.0, RSI_3_4h is less than 30.0, ROC_9_4h is less than -80.0, and STOCHRSIk_14_14_3_3_4h is greater than 40.0.

These exit signals represent different market scenarios. For instance, some conditions focus on divergences between RSI values across various timeframes, while others rely on a combination of overbought indicators with signs of trend weakness.

Function Context

The long_exit_dec function is called within the custom_exit function. This general function manages exit logic for various trading modes. The long_exit_dec function is specifically used for the "quick" mode. If long_exit_dec does not generate a sell signal, other exit functions, such as long_exit_stoploss, are evaluated.

Additional Observations

• The long_exit_dec function is not used in isolation. It is integrated into a broader trading strategy that includes other entry and exit functions.
• The specific configurations of indicators and threshold levels used in the function may vary depending on the trading strategy.
• The naming of exit signals (e.g., exit_{mode_name}_d_2_29) suggests that the code is part of a modular system where different trading "modes" may have their own exit functions.

Summary

The long_exit_dec function plays a crucial role in identifying exit opportunities for long positions within an automated trading strategy. Its logic, based on a combination of technical indicators and specific conditions, aims to protect profits by anticipating potential reversals in the asset's upward price trend.

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long_exit_stoploss function

The long_exit_stoploss function defines specific conditions to exit a long position when certain stop-loss thresholds are met. It is designed to protect against excessive losses and to ensure that market conditions support the decision to exit the position.

Key Parameters

1. profit_stake: The current profit or loss of the position in monetary terms.
2. filled_entries: A list of completed entries with their associated costs.
3. last_candle, previous_candle_*: Market data for recent candles, used to evaluate conditions.
4. trade: Information about the current trade, including its opening date and leverage.
5. current_time: Current time, for calculating trade duration.
6. stop_threshold_doom_*: Thresholds defining the maximum allowed loss, adjusted for futures or spot trading modes.

Logic and Conditions

1. "Stoploss Doom" Condition

• Purpose: Exit immediately if losses exceed a critical threshold.

• Conditions:

  • profit_stake falls below a defined limit:

    [ \text{limit} = \text{initial cost} \times \frac{\text{threshold}}{\text{leverage}} ]

  • Valid entry conditions are not met (via has_valid_entry_conditions).

  • Trade was opened after a specific date (2024-09-13) or the function is in backtesting mode.

• Example Scenario: Suppose an open position shows significant losses, and the leverage amplifies the risk of further downside. The "Stoploss Doom" condition triggers an exit to prevent catastrophic losses.

• Action: Returns True with the reason: "exit_{mode_name}_stoploss_doom".

Default Return

If none of the conditions are met, the function returns False and None, indicating that no stop-loss signal has been triggered.

Summary of Behavior

• The function prioritizes avoiding excessive losses through a critical stop-loss mechanism.
• It integrates checks to ensure the trade’s conditions remain valid and align with predefined thresholds.

This approach minimizes risk exposure and ensures the algorithm adheres to strict loss-management principles.