The MultiRSIBBand strategy is designed to backtest trading strategies using multiple relative strength index (RSI) indicators and Bollinger Bands. Here's a breakdown of what the strategy does:
The strategy defines a minimal return on investment (ROI) for different time intervals. It specifies that if the profit is not negative after 3 hours (180 minutes), the position should be sold.
If there is at least a 1% profit after 30 minutes, the position should be sold, and the same applies for a 2% profit after 20 minutes.
If there is at least a 5% profit, the position should be sold immediately.
The strategy uses a stop-loss of -20%, meaning that if the position's loss exceeds 20%, it should be sold. The strategy utilizes trailing stops, which means that if the position is in profit, the stop-loss level is adjusted to protect the profit. The trailing stop is set to 0.006 (0.6%), and the trailing stop positive offset is 0.02 (2%). This means that if the position's profit reaches 0.6%, the stop-loss level will move up, protecting a 2% profit offset. The strategy operates on a timeframe of 5 minutes. The populate_indicators function is responsible for computing the RSI indicators for two different timeframes: a short-term timeframe (1 times the ticker indicator) and a long-term timeframe (12 times the ticker indicator). It merges the computed indicators back into the main dataframe. The populate_buy_trend function populates the "buy" column in the dataframe based on certain conditions. These conditions include checking if the RSI for the long-term timeframe has increased compared to two periods ago, if the RSI for the short-term timeframe is above 50 and was below or equal to 50 in the previous period. The populate_sell_trend function is empty and needs to be implemented to define the conditions for selling the position. Overall, the strategy combines RSI indicators on different timeframes and uses Bollinger Bands to determine entry and exit points for trading positions.
If there is at least a 1% profit after 30 minutes, the position should be sold, and the same applies for a 2% profit after 20 minutes.
If there is at least a 5% profit, the position should be sold immediately.
The strategy uses a stop-loss of -20%, meaning that if the position's loss exceeds 20%, it should be sold. The strategy utilizes trailing stops, which means that if the position is in profit, the stop-loss level is adjusted to protect the profit. The trailing stop is set to 0.006 (0.6%), and the trailing stop positive offset is 0.02 (2%). This means that if the position's profit reaches 0.6%, the stop-loss level will move up, protecting a 2% profit offset. The strategy operates on a timeframe of 5 minutes. The populate_indicators function is responsible for computing the RSI indicators for two different timeframes: a short-term timeframe (1 times the ticker indicator) and a long-term timeframe (12 times the ticker indicator). It merges the computed indicators back into the main dataframe. The populate_buy_trend function populates the "buy" column in the dataframe based on certain conditions. These conditions include checking if the RSI for the long-term timeframe has increased compared to two periods ago, if the RSI for the short-term timeframe is above 50 and was below or equal to 50 in the previous period. The populate_sell_trend function is empty and needs to be implemented to define the conditions for selling the position. Overall, the strategy combines RSI indicators on different timeframes and uses Bollinger Bands to determine entry and exit points for trading positions.
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/Discord_MultiRSIBBand.py", line 43, in populate_indicators dataframe = resampled_merge(dataframe, dataframe_short) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/technical/util.py", line 92, in resampled_merge raise ValueError( ValueError: Tried to merge a faster timeframe to a slower timeframe.Upsampling is not possible.
stoploss: -0.2 timeframe: 5m hash(sha256): 32dfb19c140b674aa762213ab6afb97490b76e99ddbe59e278ba3904b9dc7285 indicators: _long _short _longrsi _shortrsi rsi
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last change: 2024-04-28 23:09:21