The StandardStrategy is a trading strategy that utilizes various indicators to generate buy and sell signals for backtesting. Here is a summary of what the strategy does:
populate_indicators: This function calculates and adds several indicators to the dataframe, including SAR, TEMA, RSI, EMA, and Bollinger Bands. It also merges an informative pair dataframe to the main dataframe.
populate_buy_trend: This function populates the "buy" column in the dataframe based on specific conditions.
It checks for a bullish trend by comparing the values of different indicators such as TEMA, SAR, RSI, and Bollinger Bands.
If all the conditions are met, it sets the "buy" column to 1. populate_sell_trend: This function populates the "sell" column in the dataframe based on specific conditions. It checks for a bearish trend by comparing the values of TEMA, SAR, EMA, and Bollinger Bands. If the conditions are met, it sets the "sell" column to 1. Overall, the strategy aims to identify potential buying opportunities when certain bullish conditions are met and selling opportunities when bearish conditions are observed. These signals can be used for backtesting and evaluating the strategy's performance.
populate_buy_trend: This function populates the "buy" column in the dataframe based on specific conditions.
It checks for a bullish trend by comparing the values of different indicators such as TEMA, SAR, RSI, and Bollinger Bands.
If all the conditions are met, it sets the "buy" column to 1. populate_sell_trend: This function populates the "sell" column in the dataframe based on specific conditions. It checks for a bearish trend by comparing the values of TEMA, SAR, EMA, and Bollinger Bands. If the conditions are met, it sets the "sell" column to 1. Overall, the strategy aims to identify potential buying opportunities when certain bullish conditions are met and selling opportunities when bearish conditions are observed. These signals can be used for backtesting and evaluating the strategy's performance.
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/StandardStrategy.py", line 164, in populate_indicators dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/strategy_helper.py", line 82, in merge_informative_pair dataframe = pd.merge_ordered(dataframe, informative, fill_method="ffill", left_on='date', ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/reshape/merge.py", line 433, in merge_ordered result = _merger(left, right) ^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/reshape/merge.py", line 402, in _merger op = _OrderedMerge( ^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/reshape/merge.py", line 1837, in __init__ _MergeOperation.__init__( File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/reshape/merge.py", line 804, in __init__ self._maybe_coerce_merge_keys() File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/reshape/merge.py", line 1483, in _maybe_coerce_merge_keys raise ValueError(msg) ValueError: You are trying to merge on datetime64[ns, UTC] and object columns for key 'date'. If you wish to proceed you should use pd.concat
stoploss: -0.3 timeframe: 1h hash(sha256): 72a83a06e1cb39cc74999545530d54abab2aea0a957f4fb778e894682996a41a indicators: upper tema bb_lowerband date tema_1w volume bb_width_past_2 date date sar_1w sar bb_width_past_3 mid bb_width tema_1w ema7 sar_1w lower bb_middleband rsi bb_upperband bb_width_past_4 bb_width_past_1 bb_width_past_5
No similar strategies found. (based on used indicators)
last change: 2024-04-28 08:18:46