The Hacklemore strategy is designed for backtesting trading strategies. It involves the following steps:
Populate Indicators: This method calculates various indicators using the input dataframe and metadata. It performs the following actions:
Initializes trade-related data for the current pair.
Checks for active trades and calculates the current profit, peak profit, and current-to-peak profit ratio for the active trade.
Checks for other open trades and calculates the average profit for those trades.
Populates additional indicators like volume mean, RMI (Relative Momentum Index), SAR (Stop and Reverse), MACD (Moving Average Convergence Divergence), rolling maximum and minimum values, upper and lower bands of Bollinger Bands, and breakout levels. Populate Buy Trend: This method determines the conditions for entering a buy trade based on the indicators and trade data. It checks the following conditions: If there is an active trade, the current-to-peak profit ratio should be greater than 0.8 and the RMI-slow value should be greater than or equal to 60. If there is no active trade, it checks various conditions like up trend, RMI-slow value, MACD histogram, breakout level, volume, moving averages, SAR, and price patterns. It also considers the average profit of other trades if they exist. Populate Sell Trend: This method determines the conditions for exiting a buy trade based on the indicators and trade data. It checks the following conditions: If there is an active trade, the current profit should be negative but greater than the stop loss value, the dn_trend should be 1, the RMI-fast value should be less than 50, and there should be positive volume. If there are other trades, it checks the average profit condition. If there is no active trade, it checks for negative volume. The strategy also includes some custom methods for ordering, price protection, and timeouts. These methods interact with the order book and current price to determine whether to execute or cancel an order. Please note that this description provides a high-level overview of the strategy's functionality.
Checks for active trades and calculates the current profit, peak profit, and current-to-peak profit ratio for the active trade.
Checks for other open trades and calculates the average profit for those trades.
Populates additional indicators like volume mean, RMI (Relative Momentum Index), SAR (Stop and Reverse), MACD (Moving Average Convergence Divergence), rolling maximum and minimum values, upper and lower bands of Bollinger Bands, and breakout levels. Populate Buy Trend: This method determines the conditions for entering a buy trade based on the indicators and trade data. It checks the following conditions: If there is an active trade, the current-to-peak profit ratio should be greater than 0.8 and the RMI-slow value should be greater than or equal to 60. If there is no active trade, it checks various conditions like up trend, RMI-slow value, MACD histogram, breakout level, volume, moving averages, SAR, and price patterns. It also considers the average profit of other trades if they exist. Populate Sell Trend: This method determines the conditions for exiting a buy trade based on the indicators and trade data. It checks the following conditions: If there is an active trade, the current profit should be negative but greater than the stop loss value, the dn_trend should be 1, the RMI-fast value should be less than 50, and there should be positive volume. If there are other trades, it checks the average profit condition. If there is no active trade, it checks for negative volume. The strategy also includes some custom methods for ordering, price protection, and timeouts. These methods interact with the order book and current price to determine whether to execute or cancel an order. Please note that this description provides a high-level overview of the strategy's functionality.
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 1335, in backtest_one_strategy results = self.backtest( ^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/optimize/backtesting.py", line 1213, in backtest data: Dict = self._get_ohlcv_as_lists(processed) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/optimize/backtesting.py", line 381, in _get_ohlcv_as_lists df_analyzed = self.strategy.ft_advise_signals(pair_data, {'pair': pair}) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 1391, in ft_advise_signals dataframe = self.advise_entry(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 1425, in advise_entry df = self.populate_entry_trend(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 225, in populate_entry_trend return self.populate_buy_trend(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/user_data/strategies/HacklemoreX.py", line 94, in populate_buy_trend trade_data = self.custom_trade_info[metadata['pair']] ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^ KeyError: 'BTC/USDT'
stoploss: -0.085 timeframe: 5m hash(sha256): 3b963b5d1090c1c3616d6b7f92c08fa027c9dd24f3cecbc14dbe4d22fd4a672e indicators: active_trade upper asks rmifast close breakoutlow current_peak_ratio macdhist midbb other_trades avg_other_profit volume max upperbb volume_mean_slow sar high macdsignal breakoutlowlast bids current_profit rmislow up_trend price macd min dn_trend lower peak_profit lowerbb runmode low
No similar strategies found. (based on used indicators)
last change: 2024-04-28 08:17:45