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update documentation and add default values
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@ -122,6 +122,27 @@ automatically accessible by including them on the indicator-list, and these incl
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- **profit_ratio :** trade profit ratio
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- **profit_abs :** absolute profit return of the trade
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#### Sample Output for Indicator Values
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```bash
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freqtrade backtesting-analysis -c user_data/config.json --analysis-groups 0 --indicator-list chikou_span tenkan_sen
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```
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In this example,
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we aim to display the `chikou_span` and `tenkan_sen` indicator values at both the entry and exit points of trades.
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A sample output for indicators might look like this:
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| pair | open_date | enter_reason | exit_reason | chikou_span (entry) | tenkan_sen (entry) | chikou_span (exit) | tenkan_sen (exit) |
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|-----------|---------------------------|--------------|-------------|---------------------|--------------------|--------------------|-------------------|
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| DOGE/USDT | 2024-07-06 00:35:00+00:00 | | exit_signal | 0.105 | 0.106 | 0.105 | 0.107 |
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| BTC/USDT | 2024-08-05 14:20:00+00:00 | | roi | 54643.440 | 51696.400 | 54386.000 | 52072.010 |
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As shown in the table, `chikou_span (entry)` represents the indicator value at the time of trade entry,
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while `chikou_span (exit)` reflects its value at the time of exit.
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This detailed view of indicator values enhances the analysis.
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The `(entry)` and `(exit)` suffixes are added to indicators
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to distinguish the values at the entry and exit points of the trade.
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### Filtering the trade output by date
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@ -51,7 +51,9 @@ def _load_exit_signal_candles(backtest_dir: Path) -> Dict[str, Dict[str, pd.Data
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return _load_backtest_analysis_data(backtest_dir, "exited")
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def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_candles, date_col: str):
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def _process_candles_and_indicators(
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pairlist, strategy_name, trades, signal_candles, date_col: str = "open_date"
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):
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analysed_trades_dict: Dict[str, Dict] = {strategy_name: {}}
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try:
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@ -69,7 +71,7 @@ def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_cand
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def _analyze_candles_and_indicators(
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pair: str, trades: pd.DataFrame, signal_candles: pd.DataFrame, date_col: str
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pair: str, trades: pd.DataFrame, signal_candles: pd.DataFrame, date_col: str = "open_date"
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) -> pd.DataFrame:
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buyf = signal_candles
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