bd6a0f05d7
- Introduced `BBRSIncrementalState` for real-time processing of the Bollinger Bands + RSI strategy, allowing minute-level data input and internal timeframe aggregation. - Added `TimeframeAggregator` class to handle real-time data aggregation to higher timeframes (15min, 1h, etc.). - Updated `README_BBRS.md` to document the new incremental strategy, including key features and usage examples. - Created comprehensive tests to validate the incremental strategy against the original implementation, ensuring signal accuracy and performance consistency. - Enhanced error handling and logging for better monitoring during real-time processing. - Updated `TODO.md` to reflect the completion of the incremental BBRS strategy implementation.
81 lines
2.8 KiB
Python
81 lines
2.8 KiB
Python
"""
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Test pandas EMA behavior to understand Wilder's smoothing initialization
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"""
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import pandas as pd
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import numpy as np
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def test_pandas_ema():
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"""Test how pandas EMA works with Wilder's smoothing."""
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# Sample data from our debug
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prices = [16568.00, 16569.00, 16569.00, 16568.00, 16565.00, 16565.00,
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16565.00, 16565.00, 16565.00, 16565.00, 16566.00, 16566.00,
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16563.00, 16566.00, 16566.00, 16566.00, 16566.00, 16566.00]
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# Calculate deltas
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deltas = np.diff(prices)
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gains = np.where(deltas > 0, deltas, 0)
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losses = np.where(deltas < 0, -deltas, 0)
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print("Price changes:")
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for i, (delta, gain, loss) in enumerate(zip(deltas, gains, losses)):
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print(f"Step {i+1}: delta={delta:5.2f}, gain={gain:4.2f}, loss={loss:4.2f}")
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# Create series
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gain_series = pd.Series(gains)
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loss_series = pd.Series(losses)
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period = 14
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alpha = 1.0 / period
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print(f"\nUsing period={period}, alpha={alpha:.6f}")
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# Test different EMA parameters
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print("\n1. Standard EMA with min_periods=period:")
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avg_gain_1 = gain_series.ewm(alpha=alpha, adjust=False, min_periods=period).mean()
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avg_loss_1 = loss_series.ewm(alpha=alpha, adjust=False, min_periods=period).mean()
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print("Index | Gain | Loss | AvgGain | AvgLoss | RS | RSI")
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print("-" * 60)
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for i in range(min(len(avg_gain_1), 18)):
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gain = gains[i] if i < len(gains) else 0
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loss = losses[i] if i < len(losses) else 0
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avg_g = avg_gain_1.iloc[i]
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avg_l = avg_loss_1.iloc[i]
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if not (pd.isna(avg_g) or pd.isna(avg_l)) and avg_l != 0:
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rs = avg_g / avg_l
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rsi = 100 - (100 / (1 + rs))
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else:
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rs = np.nan
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rsi = np.nan
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print(f"{i:5d} | {gain:4.2f} | {loss:4.2f} | {avg_g:7.4f} | {avg_l:7.4f} | {rs:4.2f} | {rsi:6.2f}")
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print("\n2. EMA with min_periods=1:")
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avg_gain_2 = gain_series.ewm(alpha=alpha, adjust=False, min_periods=1).mean()
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avg_loss_2 = loss_series.ewm(alpha=alpha, adjust=False, min_periods=1).mean()
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print("Index | Gain | Loss | AvgGain | AvgLoss | RS | RSI")
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print("-" * 60)
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for i in range(min(len(avg_gain_2), 18)):
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gain = gains[i] if i < len(gains) else 0
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loss = losses[i] if i < len(losses) else 0
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avg_g = avg_gain_2.iloc[i]
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avg_l = avg_loss_2.iloc[i]
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if not (pd.isna(avg_g) or pd.isna(avg_l)) and avg_l != 0:
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rs = avg_g / avg_l
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rsi = 100 - (100 / (1 + rs))
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elif avg_l == 0 and avg_g > 0:
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rs = np.inf
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rsi = 100.0
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else:
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rs = np.nan
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rsi = np.nan
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print(f"{i:5d} | {gain:4.2f} | {loss:4.2f} | {avg_g:7.4f} | {avg_l:7.4f} | {rs:4.2f} | {rsi:6.2f}")
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if __name__ == "__main__":
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test_pandas_ema() |