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The ichiV1_Marius_fixed strategy is a backtesting strategy that involves the following components:
populate_indicators: This function populates various indicators for the trading strategy based on the provided dataframe and metadata. It merges informative dataframes with different timeframes, calculates offset values, and sets flags for price tests and pump-dump alerts. populate_buy_trend: This function determines the buy signals for the strategy based on specific conditions.
It checks for protection measures, and if enabled, evaluates additional conditions related to Tesla stock.
If the conditions are met, the buy flag is set to 1.
populate_sell_trend: This function determines the sell signals for the strategy based on specific conditions. It checks for a crossover below a sell trend indicator, and if the condition is met, the sell flag is set to 1. df[name + '_nmin'] calculation: This line of code calculates a normalized value based on the rolling minimum and maximum values of a specific column in the dataframe. Normalization functions: These functions calculate the normalized values of the input data based on the minimum and maximum values. Heikin-Ashi (HA) calculation: This section calculates the Heikin-Ashi values for the given OHLC (Open, High, Low, Close) data. It calculates the HA Open, HA Close, HA High, and HA Low values based on the formulas provided. Smoothing: If the smoothing parameter is provided, the calculated HA values are smoothed using the Exponential Moving Average (EMA) with the specified smoothing period. Result calculation: This section calculates a binary result based on the conditions of the "test1" and "test2" columns. If both conditions are true, the result is set to 1; otherwise, it is set to 0. Overall, the ichiV1_Marius_fixed strategy combines various indicators, conditions, and calculations to generate buy and sell signals for backtesting trading strategies.
It checks for protection measures, and if enabled, evaluates additional conditions related to Tesla stock.
If the conditions are met, the buy flag is set to 1.
populate_sell_trend: This function determines the sell signals for the strategy based on specific conditions. It checks for a crossover below a sell trend indicator, and if the condition is met, the sell flag is set to 1. df[name + '_nmin'] calculation: This line of code calculates a normalized value based on the rolling minimum and maximum values of a specific column in the dataframe. Normalization functions: These functions calculate the normalized values of the input data based on the minimum and maximum values. Heikin-Ashi (HA) calculation: This section calculates the Heikin-Ashi values for the given OHLC (Open, High, Low, Close) data. It calculates the HA Open, HA Close, HA High, and HA Low values based on the formulas provided. Smoothing: If the smoothing parameter is provided, the calculated HA values are smoothed using the Exponential Moving Average (EMA) with the specified smoothing period. Result calculation: This section calculates a binary result based on the conditions of the "test1" and "test2" columns. If both conditions are true, the result is set to 1; otherwise, it is set to 0. Overall, the ichiV1_Marius_fixed strategy combines various indicators, conditions, and calculations to generate buy and sell signals for backtesting trading strategies.
stoploss: -0.275 timeframe: 5m hash(sha256): 628e96db0116aeca26bfd592177e57aa5175cbe52ad1cc22606668f38d7c88c0 indicators: fan_magnitude close ProfitMargin1 senkou_a position_adjustment_enable HA_Close sell_signal tenkan_sen HA_High volume ProfitLoss2 leading_senkou_span_a retries date close ema55 pump_period btc_threshold high pHSL weekly_close_avg trend_close_1h pump_strength_2 pump_pause_duration weekly_close_avg_1h antipump_threshold rsi_1h pump_strength sell_trend_indicator atr buy_threshold trend_open_8h ema_slow trend_open_6h trend_close_6h Smooth_HA_C HA_OpenHA_Closelow USDTBUSDUSDCDAITUSDPAXUSDEURGBP HA_Ope
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last change: 2023-07-05 16:47:59