The given code appears to be a part of a larger codebase for a backtesting website that performs backtests on trading strategies. Here's a short description of what the strategy does based on the provided code:
The strategy, implemented in a class called CryptoFrog, inherits from an IStrategy interface. It has three main methods: populate_indicators, populate_buy_trend, and populate_sell_trend.
populate_indicators method: This method takes a DataFrame and metadata as input.
It populates indicators for the trading strategy based on the provided DataFrame and metadata.
It performs calculations and transformations on the DataFrame to generate indicators. The indicators include 'roc', 'atr', 'sroc', 'ssl-dir', 'rmi-up-trend', and 'candle-up-trend'. The method returns the modified DataFrame with indicators. populate_buy_trend method: This method takes a DataFrame and metadata as input. It populates the buy signals or conditions for the trading strategy based on the provided DataFrame and metadata. It applies various conditions using the values from the DataFrame columns to determine the buy signals. If a buy signal condition is satisfied, the corresponding 'buy' column in the DataFrame is set to 1. The method returns the modified DataFrame with buy signals. populate_sell_trend method: This method takes a DataFrame and metadata as input. It populates the sell signals or conditions for the trading strategy based on the provided DataFrame and metadata. It applies various conditions using the values from the DataFrame columns to determine the sell signals. If a sell signal condition is satisfied, the corresponding 'sell' column in the DataFrame is set to 1. The method returns the modified DataFrame with sell signals. Additionally, there are some code snippets related to custom stop loss and ROI (Return on Investment) calculations. The custom stop loss determines whether to exit a trade based on specific conditions and time thresholds. The ROI calculation adjusts the minimal ROI (Return on Investment) based on different trends and pullback amounts. Please note that this description may not capture the complete functionality of the strategy, as there might be additional code outside the provided snippet.
populate_indicators method: This method takes a DataFrame and metadata as input.
It populates indicators for the trading strategy based on the provided DataFrame and metadata.
It performs calculations and transformations on the DataFrame to generate indicators. The indicators include 'roc', 'atr', 'sroc', 'ssl-dir', 'rmi-up-trend', and 'candle-up-trend'. The method returns the modified DataFrame with indicators. populate_buy_trend method: This method takes a DataFrame and metadata as input. It populates the buy signals or conditions for the trading strategy based on the provided DataFrame and metadata. It applies various conditions using the values from the DataFrame columns to determine the buy signals. If a buy signal condition is satisfied, the corresponding 'buy' column in the DataFrame is set to 1. The method returns the modified DataFrame with buy signals. populate_sell_trend method: This method takes a DataFrame and metadata as input. It populates the sell signals or conditions for the trading strategy based on the provided DataFrame and metadata. It applies various conditions using the values from the DataFrame columns to determine the sell signals. If a sell signal condition is satisfied, the corresponding 'sell' column in the DataFrame is set to 1. The method returns the modified DataFrame with sell signals. Additionally, there are some code snippets related to custom stop loss and ROI (Return on Investment) calculations. The custom stop loss determines whether to exit a trade based on specific conditions and time thresholds. The ROI calculation adjusts the minimal ROI (Return on Investment) based on different trends and pullback amounts. Please note that this description may not capture the complete functionality of the strategy, as there might be additional code outside the provided snippet.
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.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): 9d71f09877e70d51ddad5ae39adbe02e08f9b84df8becbdde351c4be31699451 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: CryptoFrogHO3A4, Similarity Score: 98.57%
Strategy: CryptoFrog_205, 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: 2024-04-29 12:55:14