The "IndividualCrossSMA" strategy is a backtesting strategy that uses simple moving averages (SMAs) to generate buy and sell signals for a list of trading pairs. Here's a breakdown of the important parts of the strategy:
The strategy uses the 1-hour timeframe for analysis. The "minimal_roi" parameter sets a fixed return on investment (ROI) of 100% for all trades.
The "stoploss" parameter sets a stop loss level of -0.05, indicating a 5% loss threshold.
The strategy uses limit orders for entry and exit, and market orders for stop loss and emergency exit.
The strategy calculates short SMAs for each trading pair based on the values specified in the "buy_params" dictionary. It also calculates long SMAs for each trading pair based on the values specified in the "sell_params" dictionary. The strategy generates entry signals when the short SMA crosses above the long SMA and the trading volume is greater than 0. It generates exit signals when the short SMA crosses below the long SMA and the trading volume is greater than 0. Overall, the strategy aims to capture potential trends by using SMA crossovers as signals for buying and selling. It sets a fixed ROI target and a stop loss level to manage risk. The strategy is applied to a list of trading pairs, and each pair has its own set of SMA parameters for generating signals.
The "stoploss" parameter sets a stop loss level of -0.05, indicating a 5% loss threshold.
The strategy uses limit orders for entry and exit, and market orders for stop loss and emergency exit.
The strategy calculates short SMAs for each trading pair based on the values specified in the "buy_params" dictionary. It also calculates long SMAs for each trading pair based on the values specified in the "sell_params" dictionary. The strategy generates entry signals when the short SMA crosses above the long SMA and the trading volume is greater than 0. It generates exit signals when the short SMA crosses below the long SMA and the trading volume is greater than 0. Overall, the strategy aims to capture potential trends by using SMA crossovers as signals for buying and selling. It sets a fixed ROI target and a stop loss level to manage risk. The strategy is applied to a list of trading pairs, and each pair has its own set of SMA parameters for generating signals.
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_IndividualCrossSMA.py", line 174, in populate_indicators dataframe = pd.concat([dataframe, pd.DataFrame(ta.SMA(dataframe, timeperiod=self.buy_params[compra]), columns=[f'{compra}_{self.buy_params[compra]}'])], axis=1) ~~~~~~~~~~~~~~~^^^^^^^^ KeyError: 'buy_sma_short_btc/usdt'
stoploss: -0.05 timeframe: 1h hash(sha256): 2abf61296b274be456a63fcb8fc468160f07e14174f1c04bbcbc70fcc156c643 indicators: volume
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last change: 2024-07-27 09:41:39