The ReinforcedSmoothScalp strategy is designed to generate a large number of potential buy signals and make small profits on each trade. Here are the key components of the strategy:
Minimal ROI: The strategy aims for a minimal return on investment (ROI) of 0.02 (2%). Stoploss: The optimal stoploss is set at -0.8 (80% loss), indicating that if a trade goes against the strategy by more than 80%, it will be stopped out.
Ticker Interval: The strategy is designed to work with ticker intervals of 1 minute.
Resample Factor: The strategy uses a resample factor of 5 to establish the general trend.
It resamples the data to determine whether the market is in an uptrend, downtrend, or sideways trend. The strategy calculates several technical indicators using the talib library and qtpylib indicators. These indicators include exponential moving averages (EMA), stochastic fast (STOCHF), average directional index (ADX), commodity channel index (CCI), relative strength index (RSI), money flow index (MFI), and Bollinger Bands. The populate_indicators function is responsible for populating these indicators in the dataframe. The populate_buy_trend function determines the conditions for a buy signal. These conditions include the price being below the EMA low, ADX being above 30, MFI being below 30, fastk and fastd being below 30 and crossed above, and the resampled simple moving average (SMA) being below the close price. The populate_sell_trend function determines the conditions for a sell signal. These conditions include the price being above the EMA high or fastk and fastd crossing above 70, and CCI being above 100. The resample function is used to resample the dataframe based on the ticker interval and resample factor. It calculates OHLC (open, high, low, close) values for the resampled timeframe and calculates a resampled SMA. Overall, the strategy focuses on generating frequent buy signals and aims to make small profits on each trade while managing risk through the stoploss.
Ticker Interval: The strategy is designed to work with ticker intervals of 1 minute.
Resample Factor: The strategy uses a resample factor of 5 to establish the general trend.
It resamples the data to determine whether the market is in an uptrend, downtrend, or sideways trend. The strategy calculates several technical indicators using the talib library and qtpylib indicators. These indicators include exponential moving averages (EMA), stochastic fast (STOCHF), average directional index (ADX), commodity channel index (CCI), relative strength index (RSI), money flow index (MFI), and Bollinger Bands. The populate_indicators function is responsible for populating these indicators in the dataframe. The populate_buy_trend function determines the conditions for a buy signal. These conditions include the price being below the EMA low, ADX being above 30, MFI being below 30, fastk and fastd being below 30 and crossed above, and the resampled simple moving average (SMA) being below the close price. The populate_sell_trend function determines the conditions for a sell signal. These conditions include the price being above the EMA high or fastk and fastd crossing above 70, and CCI being above 100. The resample function is used to resample the dataframe based on the ticker interval and resample factor. It calculates OHLC (open, high, low, close) values for the resampled timeframe and calculates a resampled SMA. Overall, the strategy focuses on generating frequent buy signals and aims to make small profits on each trade while managing risk through the stoploss.
Traceback (most recent call last): File "/freqtrade/freqtrade/main.py", line 42, in main return_code = args['func'](args) ^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/commands/optimize_commands.py", line 58, in start_backtesting backtesting.start() File "/freqtrade/freqtrade/optimize/backtesting.py", line 1401, in start min_date, max_date = self.backtest_one_strategy(strat, data, timerange) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/optimize/backtesting.py", line 1318, in backtest_one_strategy preprocessed = self.strategy.advise_all_indicators(data) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 1378, in advise_all_indicators return {pair: self.advise_indicators(pair_data.copy(), {'pair': pair}).copy() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 1378, inreturn {pair: self.advise_indicators(pair_data.copy(), {'pair': pair}).copy() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 1410, in advise_indicators return self.populate_indicators(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/user_data/strategies/ReinforcedSmoothScalp.py", line 38, in populate_indicators stoch_fast = ta.STOCHF(dataframe, 5.0, 3.0, 0.0, 3.0, 0.0) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "talib/_abstract.pxi", line 444, in talib._ta_lib.Function.__call__ File "talib/_abstract.pxi", line 310, in talib._ta_lib.Function.set_function_args File "talib/_abstract.pxi", line 513, in talib._ta_lib.Function.__check_opt_input_value TypeError: Invalid parameter value for fastk_period (expected int, got float)
stoploss: -0.8 timeframe: 1m hash(sha256): bda23ba56edadd091a31de2ed82d360f4d82bbef9c989a4a92beb5910e1f9c66 indicators: upper ema_close close mfi bb_lowerband fastk date open fastd mid ema_high cci ema_low open high low close adx lower bb_middleband rsi bb_upperband resample_sma
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last change: 2024-04-28 14:31:23