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The ClucHAnix5m strategy is a trading strategy implemented in Python using the qtpylib and ta libraries. This strategy is designed for backtesting on a 5-minute candle timeframe. Here's a brief overview of the strategy's key components:
Indicators Generation: The strategy starts by calculating various technical indicators including:
Heikin-Ashi candlestick data.
Exponential Moving Averages (EMA) of different periods (8, 14, 26).
Simple Moving Average (SMA) with a period of 15.
Relative Strength Index (RSI) with different periods (4, 14, 20). Commodity Trading Index (CTI) with a length of 20. Stochastic Fast indicator (fastd and fastk). Average Directional Index (ADX). Bollinger Bands (lower, mid) based on Heikin-Ashi typical price. Bollinger Bands (bb_lowerband2, bb_middleband2, bb_upperband2) based on typical price. Various other calculated values related to price changes and trends. Informative Data: The strategy fetches informative data for 'BTC/USDT' on the 1-hour timeframe and derives Heikin-Ashi data from it. Additional technical indicators like Rate of Change Ratio (ROCR) are calculated for this informative data. BTC/USDT Data: The strategy fetches both 1-minute and 1-day candle data for 'BTC/USDT'. The close price of BTC on the previous day is used in calculations. Custom Indicators: The strategy uses custom indicators like Zero Lag Exponential Moving Averages (zema) with periods of 30 and 200, and calculates a 'pump_strength' value based on the difference between these averages. Price Percentage Metrics: The strategy calculates price percentage changes and related metrics including averages and normalized values. Buy Trend Conditions: The strategy determines buy trend conditions using various criteria: BTC safety conditions based on percentage change. Pump strength condition with an anti-pump threshold. Multiple "lambo" conditions involving close prices and RSI thresholds. Local uptrend conditions based on moving averages, open price differences, and Bollinger Bands. NFI_32 conditions involving RSI and SMA thresholds. EWO_1 conditions using RSI, EWO value, and moving averages. EWO_low conditions based on RSI, EWO value, and moving averages. COFI conditions based on open price and EMA. Note: The description provided is a concise overview of the strategy based on the code snippet you've provided. It's important to thoroughly test and validate any trading strategy before deploying it in a real trading environment.
Exponential Moving Averages (EMA) of different periods (8, 14, 26).
Simple Moving Average (SMA) with a period of 15.
Relative Strength Index (RSI) with different periods (4, 14, 20). Commodity Trading Index (CTI) with a length of 20. Stochastic Fast indicator (fastd and fastk). Average Directional Index (ADX). Bollinger Bands (lower, mid) based on Heikin-Ashi typical price. Bollinger Bands (bb_lowerband2, bb_middleband2, bb_upperband2) based on typical price. Various other calculated values related to price changes and trends. Informative Data: The strategy fetches informative data for 'BTC/USDT' on the 1-hour timeframe and derives Heikin-Ashi data from it. Additional technical indicators like Rate of Change Ratio (ROCR) are calculated for this informative data. BTC/USDT Data: The strategy fetches both 1-minute and 1-day candle data for 'BTC/USDT'. The close price of BTC on the previous day is used in calculations. Custom Indicators: The strategy uses custom indicators like Zero Lag Exponential Moving Averages (zema) with periods of 30 and 200, and calculates a 'pump_strength' value based on the difference between these averages. Price Percentage Metrics: The strategy calculates price percentage changes and related metrics including averages and normalized values. Buy Trend Conditions: The strategy determines buy trend conditions using various criteria: BTC safety conditions based on percentage change. Pump strength condition with an anti-pump threshold. Multiple "lambo" conditions involving close prices and RSI thresholds. Local uptrend conditions based on moving averages, open price differences, and Bollinger Bands. NFI_32 conditions involving RSI and SMA thresholds. EWO_1 conditions using RSI, EWO value, and moving averages. EWO_low conditions based on RSI, EWO value, and moving averages. COFI conditions based on open price and EMA. Note: The description provided is a concise overview of the strategy based on the code snippet you've provided. It's important to thoroughly test and validate any trading strategy before deploying it in a real trading environment.
Traceback (most recent call last): File "/freqtrade/freqtrade/strategy/strategy_wrapper.py", line 27, in wrapper return f(*args, **kwargs) ^^^^^^^^^^^^^^^^^^ File "/freqtrade/user_data/strategies/ClucHAnix_BB_RPB_MOD_E0V1E_DYNAMIC_TB1.py", line 448, in confirm_trade_exit trade.sell_reason = sell_reason + "_" + trade.buy_tag ^^^^^^^^^^^^^^^^^ AttributeError: property 'sell_reason' of 'LocalTrade' object has no setter
stoploss: -0.99 timeframe: 5m hash(sha256): c953429441da55c01b6d814848099ea0719997ba76f01d362e64ebfd36323c62 indicators: upper local_trend_bb_factor lambo1_ema_14_factor close cofi_ema bb_lowerband lambo1_rsi_4_limit lambo2_rsi_4_limit volume rsi_4 date close ha_closedelta buy_tag clucha_bbdelta_close rsi_14 bb_upperband2 high ewo_low_offset ema_8 ewo_low_enabled antipump_threshold pump_strength ha_high cofi_enabled adx ema_slow ema_26 runmode ewo_candles_buy local_trend_enabled ewo_high ewo_low rsi_20 bbdelta bb_middleband2 cofi_fastk cofi_ewo_high nfi32_cti_limit closedelta zema_30 ema_fast ha_open sma_15 nfi32_
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last change: 2024-09-30 14:34:26