The "Ichimoku_v36" strategy is a trading strategy implemented in the Freqtrade library. Here's a brief description of what the strategy does:
The strategy uses the Ichimoku Cloud indicator to generate trading signals. It sets a minimal return on investment (ROI) target of 100%.
It uses a stop loss value of -1, which means no stop loss is applied.
The strategy operates on 4-hour candle intervals.
It provides informative pairs by resampling the original pair's data to a weekly timeframe. The strategy calculates various indicators, including the Tenkan-sen, Kijun-sen, Senkou Span A and B of both the original and informative dataframes. It identifies buy signals when the close price crosses above the Senkou Span A or B lines of the previous two candles and the previous two candle's close price is higher than both Senkou Span A and B. It identifies sell signals when the close price of the weekly timeframe is below the Parabolic SAR indicator value. This strategy aims to capture trends using the Ichimoku Cloud indicator and generate buy or sell signals based on the defined conditions.
It uses a stop loss value of -1, which means no stop loss is applied.
The strategy operates on 4-hour candle intervals.
It provides informative pairs by resampling the original pair's data to a weekly timeframe. The strategy calculates various indicators, including the Tenkan-sen, Kijun-sen, Senkou Span A and B of both the original and informative dataframes. It identifies buy signals when the close price crosses above the Senkou Span A or B lines of the previous two candles and the previous two candle's close price is higher than both Senkou Span A and B. It identifies sell signals when the close price of the weekly timeframe is below the Parabolic SAR indicator value. This strategy aims to capture trends using the Ichimoku Cloud indicator and generate buy or sell signals based on the defined conditions.
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/Ichimoku_v36_SAR_1s.py", line 68, 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: -1 timeframe: 4h hash(sha256): cd3487095bb8acc872e60660d66c75513fd88f0d9e850c93d8021b7ce0a9f4dc indicators: kijun_sen cloud_red tenkan CDLEVENINGDOJISTAR senkou_span_a close_1w close cloud_green senkou_a senkou_b kijun tenkan_sen sar sar_1w senkou_span_b
No similar strategies found. (based on used indicators)
last change: 2024-04-28 15:48:57