335 lines
13 KiB
Python
335 lines
13 KiB
Python
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"""
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Moving Average Indicators Comparison Test
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Focused testing for Moving Average and Exponential Moving Average implementations.
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"""
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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from datetime import datetime
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import sys
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from pathlib import Path
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# Add project root to path
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project_root = Path(__file__).parent.parent
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sys.path.insert(0, str(project_root))
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# Import original indicators
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from cycles.IncStrategies.indicators import (
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MovingAverageState as OriginalMA,
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ExponentialMovingAverageState as OriginalEMA
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)
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# Import new indicators
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from IncrementalTrader.strategies.indicators import (
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MovingAverageState as NewMA,
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ExponentialMovingAverageState as NewEMA
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)
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class MovingAverageComparisonTest:
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"""Test framework for comparing moving average implementations."""
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def __init__(self, data_file: str = "data/btcusd_1-min_data.csv", sample_size: int = 5000):
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self.data_file = data_file
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self.sample_size = sample_size
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self.data = None
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self.results = {}
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# Create results directory
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self.results_dir = Path("test/results/moving_averages")
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self.results_dir.mkdir(parents=True, exist_ok=True)
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def load_data(self):
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"""Load and prepare the data for testing."""
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print(f"Loading data from {self.data_file}...")
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df = pd.read_csv(self.data_file)
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df['datetime'] = pd.to_datetime(df['Timestamp'], unit='s')
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if self.sample_size and len(df) > self.sample_size:
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df = df.tail(self.sample_size).reset_index(drop=True)
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self.data = df
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print(f"Loaded {len(df)} data points from {df['datetime'].iloc[0]} to {df['datetime'].iloc[-1]}")
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def test_simple_moving_average(self, periods=[5, 10, 20, 50, 100]):
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"""Test Simple Moving Average implementations."""
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print("\n=== Testing Simple Moving Average ===")
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for period in periods:
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print(f"Testing SMA({period})...")
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# Initialize indicators
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original_ma = OriginalMA(period)
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new_ma = NewMA(period)
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original_values = []
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new_values = []
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prices = []
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# Process data
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for _, row in self.data.iterrows():
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price = row['Close']
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prices.append(price)
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original_ma.update(price)
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new_ma.update(price)
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original_values.append(original_ma.get_current_value() if original_ma.is_warmed_up() else np.nan)
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new_values.append(new_ma.get_current_value() if new_ma.is_warmed_up() else np.nan)
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# Store results
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self.results[f'SMA_{period}'] = {
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'original': original_values,
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'new': new_values,
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'prices': prices,
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'dates': self.data['datetime'].tolist(),
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'period': period
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}
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# Calculate differences
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diff = np.array(new_values) - np.array(original_values)
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valid_diff = diff[~np.isnan(diff)]
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if len(valid_diff) > 0:
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max_diff = np.max(np.abs(valid_diff))
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mean_diff = np.mean(np.abs(valid_diff))
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std_diff = np.std(valid_diff)
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print(f" Max difference: {max_diff:.12f}")
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print(f" Mean difference: {mean_diff:.12f}")
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print(f" Std difference: {std_diff:.12f}")
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# Status check
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if max_diff < 1e-10:
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print(f" ✅ PASSED: Mathematically equivalent")
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elif max_diff < 1e-6:
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print(f" ⚠️ WARNING: Small differences (floating point precision)")
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else:
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print(f" ❌ FAILED: Significant differences detected")
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else:
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print(f" ❌ ERROR: No valid data points")
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def test_exponential_moving_average(self, periods=[5, 10, 20, 50, 100]):
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"""Test Exponential Moving Average implementations."""
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print("\n=== Testing Exponential Moving Average ===")
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for period in periods:
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print(f"Testing EMA({period})...")
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# Initialize indicators
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original_ema = OriginalEMA(period)
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new_ema = NewEMA(period)
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original_values = []
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new_values = []
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prices = []
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# Process data
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for _, row in self.data.iterrows():
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price = row['Close']
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prices.append(price)
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original_ema.update(price)
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new_ema.update(price)
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original_values.append(original_ema.get_current_value() if original_ema.is_warmed_up() else np.nan)
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new_values.append(new_ema.get_current_value() if new_ema.is_warmed_up() else np.nan)
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# Store results
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self.results[f'EMA_{period}'] = {
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'original': original_values,
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'new': new_values,
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'prices': prices,
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'dates': self.data['datetime'].tolist(),
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'period': period
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}
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# Calculate differences
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diff = np.array(new_values) - np.array(original_values)
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valid_diff = diff[~np.isnan(diff)]
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if len(valid_diff) > 0:
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max_diff = np.max(np.abs(valid_diff))
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mean_diff = np.mean(np.abs(valid_diff))
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std_diff = np.std(valid_diff)
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print(f" Max difference: {max_diff:.12f}")
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print(f" Mean difference: {mean_diff:.12f}")
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print(f" Std difference: {std_diff:.12f}")
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# Status check
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if max_diff < 1e-10:
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print(f" ✅ PASSED: Mathematically equivalent")
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elif max_diff < 1e-6:
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print(f" ⚠️ WARNING: Small differences (floating point precision)")
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else:
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print(f" ❌ FAILED: Significant differences detected")
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else:
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print(f" ❌ ERROR: No valid data points")
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def plot_comparison(self, indicator_name: str):
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"""Plot detailed comparison for a specific indicator."""
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if indicator_name not in self.results:
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print(f"No results found for {indicator_name}")
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return
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result = self.results[indicator_name]
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dates = pd.to_datetime(result['dates'])
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# Create figure with subplots
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fig, axes = plt.subplots(3, 1, figsize=(15, 12))
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fig.suptitle(f'{indicator_name} - Detailed Comparison Analysis', fontsize=16)
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# Plot 1: Price and indicators
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ax1 = axes[0]
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ax1.plot(dates, result['prices'], label='Price', alpha=0.6, color='gray')
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ax1.plot(dates, result['original'], label='Original', alpha=0.8, linewidth=2)
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ax1.plot(dates, result['new'], label='New', alpha=0.8, linewidth=2, linestyle='--')
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ax1.set_title(f'{indicator_name} vs Price')
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ax1.legend()
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ax1.grid(True, alpha=0.3)
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# Plot 2: Overlay comparison (zoomed)
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ax2 = axes[1]
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ax2.plot(dates, result['original'], label='Original', alpha=0.8, linewidth=2)
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ax2.plot(dates, result['new'], label='New', alpha=0.8, linewidth=2, linestyle='--')
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ax2.set_title(f'{indicator_name} Values Comparison (Detailed)')
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ax2.legend()
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ax2.grid(True, alpha=0.3)
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# Plot 3: Difference analysis
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ax3 = axes[2]
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diff = np.array(result['new']) - np.array(result['original'])
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ax3.plot(dates, diff, color='red', alpha=0.7, linewidth=1)
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ax3.set_title(f'{indicator_name} Difference (New - Original)')
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ax3.axhline(y=0, color='black', linestyle='-', alpha=0.5)
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ax3.grid(True, alpha=0.3)
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# Add statistics text
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valid_diff = diff[~np.isnan(diff)]
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if len(valid_diff) > 0:
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stats_text = f'Max: {np.max(np.abs(valid_diff)):.2e}\n'
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stats_text += f'Mean: {np.mean(np.abs(valid_diff)):.2e}\n'
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stats_text += f'Std: {np.std(valid_diff):.2e}'
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ax3.text(0.02, 0.98, stats_text, transform=ax3.transAxes,
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verticalalignment='top', bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.8))
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# Format x-axis
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for ax in axes:
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ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
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ax.xaxis.set_major_locator(mdates.DayLocator(interval=max(1, len(dates)//10)))
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plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)
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plt.tight_layout()
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# Save plot
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plot_path = self.results_dir / f"{indicator_name}_detailed_comparison.png"
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plt.savefig(plot_path, dpi=300, bbox_inches='tight')
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print(f"Plot saved to {plot_path}")
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plt.show()
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def plot_all_comparisons(self):
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"""Plot comparisons for all tested indicators."""
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print("\n=== Generating Detailed Comparison Plots ===")
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for indicator_name in self.results.keys():
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print(f"Plotting {indicator_name}...")
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self.plot_comparison(indicator_name)
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plt.close('all')
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def generate_report(self):
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"""Generate detailed report for moving averages."""
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print("\n=== Generating Moving Average Report ===")
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report_lines = []
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report_lines.append("# Moving Average Indicators Comparison Report")
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report_lines.append(f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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report_lines.append(f"Data file: {self.data_file}")
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report_lines.append(f"Sample size: {len(self.data)} data points")
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report_lines.append("")
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# Summary table
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report_lines.append("## Summary Table")
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report_lines.append("| Indicator | Period | Max Diff | Mean Diff | Status |")
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report_lines.append("|-----------|--------|----------|-----------|--------|")
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for indicator_name, result in self.results.items():
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diff = np.array(result['new']) - np.array(result['original'])
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valid_diff = diff[~np.isnan(diff)]
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if len(valid_diff) > 0:
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max_diff = np.max(np.abs(valid_diff))
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mean_diff = np.mean(np.abs(valid_diff))
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if max_diff < 1e-10:
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status = "✅ PASSED"
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elif max_diff < 1e-6:
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status = "⚠️ WARNING"
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else:
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status = "❌ FAILED"
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report_lines.append(f"| {indicator_name} | {result['period']} | {max_diff:.2e} | {mean_diff:.2e} | {status} |")
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else:
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report_lines.append(f"| {indicator_name} | {result['period']} | N/A | N/A | ❌ ERROR |")
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report_lines.append("")
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# Detailed analysis
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report_lines.append("## Detailed Analysis")
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for indicator_name, result in self.results.items():
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report_lines.append(f"### {indicator_name}")
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diff = np.array(result['new']) - np.array(result['original'])
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valid_diff = diff[~np.isnan(diff)]
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if len(valid_diff) > 0:
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report_lines.append(f"- **Period**: {result['period']}")
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report_lines.append(f"- **Valid data points**: {len(valid_diff)}")
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report_lines.append(f"- **Max absolute difference**: {np.max(np.abs(valid_diff)):.12f}")
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report_lines.append(f"- **Mean absolute difference**: {np.mean(np.abs(valid_diff)):.12f}")
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report_lines.append(f"- **Standard deviation**: {np.std(valid_diff):.12f}")
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report_lines.append(f"- **Min difference**: {np.min(valid_diff):.12f}")
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report_lines.append(f"- **Max difference**: {np.max(valid_diff):.12f}")
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# Percentile analysis
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percentiles = [1, 5, 25, 50, 75, 95, 99]
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perc_values = np.percentile(np.abs(valid_diff), percentiles)
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perc_str = ", ".join([f"P{p}: {v:.2e}" for p, v in zip(percentiles, perc_values)])
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report_lines.append(f"- **Percentiles**: {perc_str}")
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report_lines.append("")
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# Save report
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report_path = self.results_dir / "moving_averages_report.md"
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with open(report_path, 'w', encoding='utf-8') as f:
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f.write('\n'.join(report_lines))
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print(f"Report saved to {report_path}")
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def run_tests(self):
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"""Run all moving average tests."""
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print("Starting Moving Average Comparison Tests...")
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# Load data
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self.load_data()
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# Run tests
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self.test_simple_moving_average()
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self.test_exponential_moving_average()
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# Generate outputs
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self.plot_all_comparisons()
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self.generate_report()
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print("\n✅ Moving Average tests completed!")
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if __name__ == "__main__":
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tester = MovingAverageComparisonTest(sample_size=3000)
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tester.run_tests()
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