e5c2988d71
- Refactored the Backtest class to encapsulate state and behavior, enhancing clarity and maintainability. - Updated strategy functions to accept the Backtest instance, streamlining data access and manipulation. - Introduced a new plotting method in BacktestCharts for visualizing close prices with trend indicators. - Improved handling of meta_trend data to ensure proper visualization and trend representation. - Adjusted main execution logic to support the new Backtest structure and enhanced debugging capabilities.
71 lines
2.9 KiB
Python
71 lines
2.9 KiB
Python
import os
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import matplotlib.pyplot as plt
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import seaborn as sns
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import pandas as pd
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import numpy as np
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class BacktestCharts:
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@staticmethod
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def plot(df, meta_trend):
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"""
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Plot close price line chart with a bar at the bottom: green when trend is 1, red when trend is 0.
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The bar stays at the bottom even when zooming/panning.
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- df: DataFrame with columns ['close', ...] and a datetime index or 'timestamp' column.
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- meta_trend: array-like, same length as df, values 1 (green) or 0 (red).
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"""
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fig, (ax_price, ax_bar) = plt.subplots(
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nrows=2, ncols=1, figsize=(16, 8), sharex=True,
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gridspec_kw={'height_ratios': [12, 1]}
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)
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sns.lineplot(x=df.index, y=df['close'], label='Close Price', color='blue', ax=ax_price)
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ax_price.set_title('Close Price with Trend Bar (Green=1, Red=0)')
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ax_price.set_ylabel('Price')
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ax_price.grid(True, alpha=0.3)
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ax_price.legend()
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# Clean meta_trend: ensure only 0/1, handle NaNs by forward-fill then fill remaining with 0
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meta_trend_arr = np.asarray(meta_trend)
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if not np.issubdtype(meta_trend_arr.dtype, np.number):
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meta_trend_arr = pd.Series(meta_trend_arr).astype(float).to_numpy()
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if np.isnan(meta_trend_arr).any():
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meta_trend_arr = pd.Series(meta_trend_arr).fillna(method='ffill').fillna(0).astype(int).to_numpy()
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else:
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meta_trend_arr = meta_trend_arr.astype(int)
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meta_trend_arr = np.where(meta_trend_arr != 1, 0, 1) # force only 0 or 1
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if hasattr(df.index, 'to_numpy'):
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x_vals = df.index.to_numpy()
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else:
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x_vals = np.array(df.index)
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# Find contiguous regions
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regions = []
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start = 0
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for i in range(1, len(meta_trend_arr)):
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if meta_trend_arr[i] != meta_trend_arr[i-1]:
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regions.append((start, i-1, meta_trend_arr[i-1]))
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start = i
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regions.append((start, len(meta_trend_arr)-1, meta_trend_arr[-1]))
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# Draw red vertical lines at the start of each new region (except the first)
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for region_idx in range(1, len(regions)):
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region_start = regions[region_idx][0]
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ax_price.axvline(x=x_vals[region_start], color='black', linestyle='--', alpha=0.7, linewidth=1)
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for start, end, trend in regions:
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color = '#089981' if trend == 1 else '#F23645'
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# Offset by 1 on x: span from x_vals[start] to x_vals[end+1] if possible
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x_start = x_vals[start]
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x_end = x_vals[end+1] if end+1 < len(x_vals) else x_vals[end]
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ax_bar.axvspan(x_start, x_end, color=color, alpha=1, ymin=0, ymax=1)
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ax_bar.set_ylim(0, 1)
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ax_bar.set_yticks([])
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ax_bar.set_ylabel('Trend')
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ax_bar.set_xlabel('Time')
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ax_bar.grid(False)
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ax_bar.set_title('Meta Trend')
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plt.tight_layout(h_pad=0.1)
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plt.show()
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