The "fahmibah" strategy implemented in the backtesting website includes the following key steps:
Indicators Population: The strategy starts by populating various indicators based on the provided input dataframe and metadata. These indicators include Heikin-Ashi candlestick values (open, close, high, low), Bollinger Bands (mid, lower), and other derived values such as bb_20_std2, bbdelta, closedelta, tail, bb_lowerband, ema_slow, rocr, and ema_200. Additionally, informative data on a higher timeframe (1 hour) is obtained and merged with the current timeframe.
Buy Trend Population: The strategy defines different conditions for identifying buy signals and assigns a "buy_tag" to the respective rows in the dataframe.
The conditions for potential buy signals include: a.
Fahmi1: The close price is above the exponential moving average (ema_200) and below a specific lower Bollinger Band threshold (bb20_2_low * fahmi1_lower). Furthermore, the difference between the open and close prices is less than the difference between the upper and lower Bollinger Bands two periods ago. b. Fahmi2: Similar to Fahmi1, but without considering the difference between the open and close prices. c. ClucHA: A combination of conditions involving the relative strength index (rocr_1h), Bollinger Bands, close price, and other factors. It checks for specific patterns related to the lower Bollinger Band, delta values, and the position of the close price relative to the Bollinger Bands and exponential moving average. If any of the conditions are met, a binary "buy" signal is assigned to the respective row in the dataframe. Sell Trend Population: The strategy populates the sell signals by assigning a "sell" value of 0 to all rows in the dataframe. In summary, the "fahmibah" strategy calculates various indicators, identifies potential buy signals based on specific conditions, and leaves the sell signals as 0, indicating no selling action.
Buy Trend Population: The strategy defines different conditions for identifying buy signals and assigns a "buy_tag" to the respective rows in the dataframe.
The conditions for potential buy signals include: a.
Fahmi1: The close price is above the exponential moving average (ema_200) and below a specific lower Bollinger Band threshold (bb20_2_low * fahmi1_lower). Furthermore, the difference between the open and close prices is less than the difference between the upper and lower Bollinger Bands two periods ago. b. Fahmi2: Similar to Fahmi1, but without considering the difference between the open and close prices. c. ClucHA: A combination of conditions involving the relative strength index (rocr_1h), Bollinger Bands, close price, and other factors. It checks for specific patterns related to the lower Bollinger Band, delta values, and the position of the close price relative to the Bollinger Bands and exponential moving average. If any of the conditions are met, a binary "buy" signal is assigned to the respective row in the dataframe. Sell Trend Population: The strategy populates the sell signals by assigning a "sell" value of 0 to all rows in the dataframe. In summary, the "fahmibah" strategy calculates various indicators, identifies potential buy signals based on specific conditions, and leaves the sell signals as 0, indicating no selling action.
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/fahmibah_270.py", line 160, in populate_indicators dataframe['bb20_2_low'] = bb_20_std2['lower'] ~~~~~~~~~~^^^^^^^^^ TypeError: tuple indices must be integers or slices, not str
stoploss: -0.1 timeframe: 5m hash(sha256): 91e8a9b3af27f71ad2ba9ddec95c692ce447f4a99da0776df5aefcaf6c2e683e indicators: rocr_1h upper clucha_enabled ha_low ema_200 close tail bb_lowerband bbdelta fahmi1_enabled closedelta rocr open ha_open bb20_2_low bb20_2_upp high mid ha_close bb20_2_mid ha_high lower bb_middleband ema_slow low
No similar strategies found. (based on used indicators)
last change: 2024-04-29 21:21:19