NostalgiaForInfinityX5 analysis (WIP)

Configuration

Entry protections

global_protections_long_pump
global_protections_short_pump

Entry signals

Long signals

Short signals

Normal mode

Condition 501 - Normal mode (Short)
Condition 502 - Normal mode (Short)
Condition 503 - Normal mode (Short)
Condition 504 - Normal mode (Short)

Top coins mode

Condition 641 - Top Coins mode (Short)
Condition 642 - Top Coins mode (Short)

Position adjustment modes

Grind
Rebuy
Derisk

Exit signals

Tags
Long exit normal function

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

Entry protections

global_protections_long_pump

Summary of Implemented Protections

The "pump" signal is designed to safeguard against specific scenarios where entering a long position would be highly risky. This is achieved through a combination of indicators that analyze overbought conditions, weak corrections, and unsustainable trends across various timeframes.

The primary indicators used include:

RSI (Relative Strength Index): Evaluates overbought or oversold conditions.
Aroon: Determines trend strength.
Stochastic RSI: An adapted RSI for greater sensitivity to changes.
ROC (Rate of Change): Measures the speed of price changes.
WILLIAMS %R: Another overbought/oversold indicator.

Scenario Examples and How the Signal Provides Protection

1. Avoiding Entries in Overbought Markets

Conditions:

RSI_3 > 60 on the 1-day timeframe.
AROONU_14_1d < 75 (weak or exhausted uptrend).
ROC_9_1d < 40 (insufficient momentum).

Example:
An asset shows strong upward movement over the past two days, with RSI and ROC indicating that the trend is losing strength. While the price may seem attractive due to its recent momentum, the algorithm identifies that entering at this point could mean buying near a local top.

Protection:
The combination of high RSI, weakened Aroon trend, and low momentum (ROC) prevents entry when the price is likely near a reversal point.

2. False Correction Signals

Conditions:

RSI_14_15m < 30 (mild oversold condition on the 15-minute timeframe).
AROONU_14_15m < 50 (mild downtrend).
STOCHk_14_3_3_15m < 20 (additional confirmation of oversold condition).

Example:
An asset shows slight downward movement over the past hour, but the volume is low, and longer timeframes (such as 4h and 1d) indicate that the price remains high or overbought.

Protection:
Although the 15-minute timeframe suggests oversold conditions, the lack of alignment with higher timeframes prevents premature entry, safeguarding against a potential continued pullback.

3. Unsustainable Momentum

Conditions:

RSI_3_4h > 50 (mild overbought condition on the 4-hour timeframe).
ROC_9_4h < 30 (weakened momentum on the 4-hour timeframe).
AROONU_14_4h < 75 (uptrend lacks solid strength).

Example:
The price has risen rapidly in the last 4 hours, but the rate of change (ROC) is starting to decelerate. Additionally, Aroon indicates that the uptrend is not strong enough to guarantee its continuation.

Protection:
The algorithm blocks entry to avoid exposure to a move that could reverse abruptly due to insufficient momentum support.

4. Timeframe Divergence

Conditions:

RSI_14_15m < 30 (oversold on the 15-minute timeframe).
RSI_3_1h > 45 (overbought on the 1-hour timeframe).
RSI_3_4h > 50 (overbought on the 4-hour timeframe).

Example:
An asset appears oversold on the 15-minute timeframe, but higher timeframes indicate persistent overbought conditions. This suggests that the correction in the 15-minute timeframe is minor, merely a small pullback within an overall overbought trend.

Protection:
The algorithm avoids decisions based on a single timeframe, ensuring a more comprehensive view of the market.

Conclusion

The "pump" signal primarily protects against:

Entries at local tops.
Minor pullbacks that do not present real opportunities.
Uptrends with weakened momentum.
Divergences across timeframes that suggest elevated risks.

This strategy minimizes the risk of getting caught in unfavorable price movements by leveraging a multi-indicator, multi-timeframe approach.

global_protections_short_pump

TODO

Entry signals

Condition #1 - Normal mode (Long)

Analysis of the Entry Signal

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.
(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.
(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.
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

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

Suggestions for Improvement or Consideration

Volume Confirmation: Add a volume-based condition (e.g., high volume during the close below Bollinger Band) to validate the signal.
Dynamic EMA Thresholds: Consider adjusting the 0.030 multiplier dynamically based on recent volatility to improve adaptability.
Exit Strategy: Define clear profit-taking or stop-loss rules, such as targeting the middle Bollinger Band (mean reversion) or the EMA 12 level.
Add Reversal Triggers: Include a bullish confirmation signal (e.g., RSI crossing above a threshold) to improve timing and reduce entry risk.
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.

Condition #2 - Normal mode (Long)

Analysis of the Entry Signal

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.
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.
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

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.
Clear Price Threshold: The EMA 20 condition filters entries based on bearish price action, ensuring alignment with a defined price context.
Simplicity: This setup uses straightforward conditions that are computationally efficient and easy to interpret.

Suggestions for Improvement or Consideration

Volume Confirmation: Adding a volume condition (e.g., high or increasing volume near the signal) could enhance confidence in a potential reversal.
Trend Filter: Consider incorporating a broader trend filter (e.g., EMA 50 > EMA 200) to ensure the signal aligns with the overall market direction.
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.
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.
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.

Condition #3 - Normal mode (Long)

Analysis of the Entry Signal

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.
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.
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.
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

Momentum-Based Filtering: The combination of two RSI indicators ensures the signal captures both long-term and short-term bearish momentum, improving accuracy.
Trend Confirmation: The Aroon Up condition filters out situations where bullish activity might resume prematurely.
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

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

Condition #4 - Normal mode (Long)

Analysis of the Entry Signal

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.
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.
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.
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

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

Suggestions for Improvement or Consideration

Volume Condition: Add a volume-based filter (e.g., above average volume) to confirm that the signal aligns with increased market participation.
Trend Filter: Implement a higher timeframe trend filter (e.g., EMA 50 > EMA 200) to avoid countertrend trades in strong bear markets.
Dynamic Multipliers: Adjust the 0.942 and 0.960 multipliers based on recent volatility (e.g., ATR) to enhance adaptability across different market conditions.
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.
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.

Condition 5 - Normal mode (Long)

Analysis of the Entry Signal

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.
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.
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.
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.
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.
(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.
(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

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.
Multi-Condition Filtering: The EMA gap thresholds provide additional confirmation, filtering out weaker trends and improving reliability.

Suggestions for Improvement or Consideration

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.
Dynamic Thresholds: Consider making the EMA gap conditions dynamic based on market volatility to adapt to different trading environments.
Volume Confirmation: Add a condition for high or increasing volume to validate the signal’s reliability, especially in oversold conditions.
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.

Condition 6 - Normal mode (Long)

Analysis of the Entry Signal

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.
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.
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.
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

Additional Confirmation: Consider adding another indicator to confirm the rebound, such as an RSI divergence or a moving average crossover.
Exit Plan: Ensure the exit conditions are aligned with this logic to avoid holding positions if the market continues to fall.
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.

Condition #41 - Quick mode (Long)

Analysis of the Entry Signal

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.
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.
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.
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

Momentum and Trend Convergence: The combination of RSI, Aroon, and EMA conditions ensures that momentum and trend signals align to indicate a bearish environment.
Confirmation of Oversold Conditions: The RSI and Aroon indicators effectively highlight potential oversold areas for a reversal or mean reversion.
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

Refinement of RSI Threshold: Test different RSI levels (e.g., <30) to capture deeper oversold conditions, especially in strong bearish trends.
Volume Filter: Add a condition based on volume (e.g., higher-than-average volume) to confirm participation during potential reversal points.
Trend Context: Introduce a higher timeframe trend filter (e.g., EMA 50 > EMA 200) to avoid taking long trades during strong bearish markets.
Dynamic Multipliers: Consider adapting the 0.960 multiplier for the EMA condition based on recent volatility (e.g., ATR).
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.
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.

Condition #42 - Quick mode (Long)

Analysis of the Entry Signal

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.
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.
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.
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.
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.
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

Multi-Timeframe Confirmation: The use of both current and 1-hour timeframe indicators ensures alignment of momentum signals, increasing reliability.
Volatility Awareness: The Bollinger Bandwidth condition ensures that trades are only considered in high-volatility environments, where significant price moves are more likely.
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

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.
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.
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.
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.
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.

Condition #43 - Quick mode (Long)

Analysis of the Entry Signal

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.
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.
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.
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.
(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.
(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.
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.
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

Momentum and Volume Confirmation: The RSI and MFI together provide a robust view of price and volume-based momentum.
Trend Reinforcement: The EMA conditions ensure that trades align with a strong bearish trend.
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

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.
Trend Continuation Filter:
- Add a higher timeframe trend filter to ensure the trade aligns with broader market direction, reducing false signals.
Volume Confirmation:
- Introduce a volume condition (e.g., df["volume"] > df["volume"].rolling(20).mean()) to confirm market participation.
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.
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.

Condition 120 - Grind mode (Long)

Analysis of the Entry Signal

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.
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.
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.
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

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

Suggestions for Improvement or Consideration

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.
Reversal Signal: Consider including a trigger for a reversal confirmation (e.g., a bullish candlestick pattern or a moving average crossover).
Exit Strategy Alignment: Ensure that the exit logic complements the entry signal by defining clear profit targets or using mean reversion thresholds.
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.

Condition #143 - Top Coins mode (Long)

Analysis of the Entry Signal

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.
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.
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.
(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.
(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

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.
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.
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

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.
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()).
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.
Exit Strategy:
- Define clear exit rules, such as:
- RSI 3 exceeds 70.
- Price crosses above the 12 EMA.
- Stochastic RSI %K exceeds 80.
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)

TODO

Condition #502 - Normal mode (Short)

TODO

Condition #503 - Normal mode (Short)

TODO

Condition #504 - Normal mode (Short)

TODO

Condition #542 - Quick mode (Short)

TODO

Condition #543 - Rapid mode (Short)

TODO

Condition #641 - Top Coins mode (Short)

TODO

Condition #642 - Top Coins mode (Short)

TODO

Position adjustment modes

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.

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.

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

TODO tags

exit_{mode_name}_stoploss_doom
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:

long_exit_signals:
- Used for basic or original exit signals based on specific criteria.
long_exit_main:
- The main logic for detecting whether the trade should exit, likely using more refined or frequently checked conditions.
long_exit_williams_r:
- Uses the Williams %R indicator for determining whether to exit. This indicator detects overbought or oversold conditions.
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

Flexible Structure: The function is modular, allowing easy addition or modification of exit conditions.
Profit Optimization: The logic favors maximizing profit before closing a trade, showing a preference for efficiency over immediate closure.
Dynamic Adjustments: By relying on self.target_profit_cache, the function dynamically tracks and updates profit targets for optimal results.
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

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

Suggestions for Improvement

Scalability: Consider modularizing signal conditions into separate functions for easier maintenance and testing.
Parameterization: Use configuration files or parameters for thresholds like RSI levels (e.g., 84.0, 88.0), allowing flexibility without code changes.
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:

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

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.
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.
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.
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.
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.
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.

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.

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

profit_stake: The current profit or loss of the position in monetary terms.
filled_entries: A list of completed entries with their associated costs.
last_candle, previous_candle_*: Market data for recent candles, used to evaluate conditions.
trade: Information about the current trade, including its opening date and leverage.
current_time: Current time, for calculating trade duration.
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.

Canx/NFIX5_analysis.md

NostalgiaForInfinityX5 analysis (WIP)

Configuration

Entry protections

Entry signals

Long signals

Normal mode

Quick mode

Grind mode

Top coins mode

Short signals

Normal mode

Quick and rapid mode

Top coins mode

Position adjustment modes

Exit signals

Config.json

Entry protections

global_protections_long_pump

Summary of Implemented Protections

Scenario Examples and How the Signal Provides Protection

1. Avoiding Entries in Overbought Markets

2. False Correction Signals

3. Unsustainable Momentum

4. Timeframe Divergence

Conclusion

global_protections_short_pump

Entry signals

Condition #1 - Normal mode (Long)

Analysis of the Entry Signal

Overall Strategy Analysis

Strengths

Suggestions for Improvement or Consideration

Conclusion

Condition #2 - Normal mode (Long)

Analysis of the Entry Signal

Overall Strategy Analysis

Strengths

Suggestions for Improvement or Consideration

Conclusion

Condition #3 - Normal mode (Long)

Analysis of the Entry Signal

Overall Strategy Analysis

Strengths

Suggestions for Improvement or Consideration

Conclusion

Condition #4 - Normal mode (Long)

Analysis of the Entry Signal

Overall Strategy Analysis

Strengths

Suggestions for Improvement or Consideration

Conclusion

Condition 5 - Normal mode (Long)

Analysis of the Entry Signal

Overall Strategy Analysis

Strengths

Suggestions for Improvement or Consideration

Conclusion

Condition 6 - Normal mode (Long)

Analysis of the Entry Signal

Overall Strategy Analysis

Suggestions for Improvement or Consideration

Condition #41 - Quick mode (Long)

Analysis of the Entry Signal

Overall Strategy Analysis

Strengths

Suggestions for Improvement or Consideration

Conclusion

Condition #42 - Quick mode (Long)

Analysis of the Entry Signal

Overall Strategy Analysis

Strengths

Suggestions for Improvement or Consideration

Conclusion

Condition #43 - Quick mode (Long)

Analysis of the Entry Signal

Overall Strategy Analysis

Strengths

Suggestions for Improvement or Consideration

Conclusion

Analysis of the Function `long_exit_williams_r`