The given strategy is a backtesting strategy called "CryptoFrog" designed for trading cryptocurrencies. It implements the "IStrategy" interface. Here's a brief description of what the strategy does:
populate_indicators: This method is responsible for calculating and populating various indicators used in the strategy.
It takes a DataFrame and metadata as input and returns the updated DataFrame.
populate_buy_trend: This method determines the conditions for a buy signal in the strategy.
It sets the 'buy' column of the DataFrame to 1 based on multiple conditions involving different indicators and price data. populate_sell_trend: This method determines the conditions for a sell signal in the strategy. It sets the 'sell' column of the DataFrame to 1 based on certain conditions related to indicators and price data. Custom Stoploss: This section calculates the trade duration and checks if certain conditions are met to trigger a stop loss for the trade. Freqtrade ROI Overload: This section deals with defining the minimal return on investment (ROI) and dynamically adjusting it based on trade duration and other indicators such as RMI (Relative Momentum Index), SSL (Squeeze Momentum Indicator), and candle trends. Overall, the strategy uses a combination of indicators and price data to generate buy and sell signals, and it incorporates stop loss and dynamic ROI functionality to manage trades.
It takes a DataFrame and metadata as input and returns the updated DataFrame.
populate_buy_trend: This method determines the conditions for a buy signal in the strategy.
It sets the 'buy' column of the DataFrame to 1 based on multiple conditions involving different indicators and price data. populate_sell_trend: This method determines the conditions for a sell signal in the strategy. It sets the 'sell' column of the DataFrame to 1 based on certain conditions related to indicators and price data. Custom Stoploss: This section calculates the trade duration and checks if certain conditions are met to trigger a stop loss for the trade. Freqtrade ROI Overload: This section deals with defining the minimal return on investment (ROI) and dynamically adjusting it based on trade duration and other indicators such as RMI (Relative Momentum Index), SSL (Squeeze Momentum Indicator), and candle trends. Overall, the strategy uses a combination of indicators and price data to generate buy and sell signals, and it incorporates stop loss and dynamic ROI functionality to manage trades.
Traceback (most recent call last): File "index.pyx", line 598, in pandas._libs.index.DatetimeEngine.get_loc File "pandas/_libs/hashtable_class_helper.pxi", line 2606, in pandas._libs.hashtable.Int64HashTable.get_item File "pandas/_libs/hashtable_class_helper.pxi", line 2630, in pandas._libs.hashtable.Int64HashTable.get_item KeyError: 1709312460000000000 During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/indexes/base.py", line 3791, in get_loc return self._engine.get_loc(casted_key) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "index.pyx", line 566, in pandas._libs.index.DatetimeEngine.get_loc File "index.pyx", line 600, in pandas._libs.index.DatetimeEngine.get_loc KeyError: Timestamp('2024-03-01 17:01:00+0000', tz='UTC') The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/indexes/datetimes.py", line 631, in get_loc return Index.get_loc(self, key) ^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/indexes/base.py", line 3798, in get_loc raise KeyError(key) from err KeyError: Timestamp('2024-03-01 17:01:00+0000', tz='UTC') The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/freqtrade/freqtrade/strategy/strategy_wrapper.py", line 27, in wrapper return f(*args, **kwargs) ^^^^^^^^^^^^^^^^^^ File "/freqtrade/user_data/strategies/CryptoFrog_205.py", line 375, in custom_stoploss sroc = self.custom_trade_info[trade.pair]['sroc'].loc[current_time]['sroc'] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/indexing.py", line 1153, in __getitem__ return self._getitem_axis(maybe_callable, axis=axis) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/indexing.py", line 1393, in _getitem_axis return self._get_label(key, axis=axis) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/indexing.py", line 1343, in _get_label return self.obj.xs(label, axis=axis) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/generic.py", line 4236, in xs loc = index.get_loc(key) ^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/indexes/datetimes.py", line 633, in get_loc raise KeyError(orig_key) from err KeyError: datetime.datetime(2024, 3, 1, 17, 1, tzinfo=datetime.timezone.utc)
stoploss: -0.085 timeframe: 5m hash(sha256): 9df6d7dfa478dae1194d2c04558ab9a1685ce0ed82b010b87f9e95350bbca7dd indicators: upper close HA_Close bb_lowerband date candleuptrend HA_High volume smaHigh sslDown DI high sslUp emac date sroc atr adx openclosehigh hhlow roc time any runmode Smooth_HA_C active_trade HA_OpenHA_Closelow emao_1h sqzmi HA_OpenHA_Closehigh maxdown Smooth_HA_L srsi_k rmiuptrend date roc candleuptrend hlv lower roc rsi emac_1h HA_Low date open high low close volume emac emao srsi_d hhopen mfi date ssldir candleup price_side ATR dmi_minus maxup emao sar smaLow ssldir mid bb_width Smooth_HA_H hhclos
Similar Strategies: (based on used indicators)
Strategy: CryptoFrog, Similarity Score: 98.57%
Strategy: CryptoFrogHO3A4, Similarity Score: 98.57%
Strategy: Discord_2_CryptoFrog, Similarity Score: 94.29%
Strategy: Discord_3_CryptoFrog, Similarity Score: 94.29%
Strategy: Discord_AwesomeStrategy, Similarity Score: 94.29%
Strategy: Discord_CryptoFrog1, Similarity Score: 78.57%
last change: 2024-04-28 14:45:21