Quick Start Guide

Get started with Trade Geometry Analyzer in 5 minutes.

Installation

pip install trade-geometry-analyzer

Minimal Example

import pandas as pd
from signal_analyzer import analyze, AnalysisConfig

# Your OHLC data with signals
df = pd.DataFrame({
    'Open': [...],
    'High': [...],
    'Low': [...],
    'Close': [...],
    'sig': [1, -1, 0, 1, ...],  # +1=long, -1=short, 0=neutral
})

# Configure analysis
config = AnalysisConfig(
    H=20,                        # 20-bar forward horizon
    sections=['A', 'B', 'C', 'D'],  # Run these sections
    store_paths=True,            # Required for Section D
)

# Run analysis
result = analyze(df, sig_col='sig', config=config)

# Access results
print(result.section_a['long']['metrics'])
print(result.section_d['long']['best_zones'][:5])

# Show plots
import matplotlib.pyplot as plt
plt.show()

With ATR Normalization

For volatility-adjusted analysis:

# Add ATR column to your data
df['atr'] = calculate_atr(df)  # Your ATR calculation

config = AnalysisConfig(
    H=20,
    sections=['A', 'B', 'C', 'D', 'E'],  # Include Section E
    vol_col='atr',                       # Enable vol normalization
    store_paths=True,
)

result = analyze(df, sig_col='sig', config=config)

Understanding the Output

AnalysisResult Structure

The analyze() function returns an AnalysisResult object with:

# Trade sets
result.long_trades   # TradeSet for long entries
result.short_trades  # TradeSet for short entries

# Section results (dicts with 'long' and 'short' keys)
result.section_a  # Geometry metrics
result.section_b  # Frontier analysis
result.section_c  # Ordering analysis
result.section_d  # TP/SL feasibility
result.section_e  # Vol normalization
result.section_f  # Cluster analysis

# All plots
result.plots  # Dict of matplotlib figures

Accessing Metrics

# Geometry metrics (Section A)
metrics = result.section_a['long']['metrics']
print(f"Median MFE: {metrics['median_mfe']:.2f}%")
print(f"Win Rate: {metrics['win_rate']*100:.1f}%")

# Best TP/SL zones (Section D)
best_zones = result.section_d['long']['best_zones']
for zone in best_zones[:3]:
    print(f"TP={zone['tp']:.2f}%, SL={zone['sl']:.2f}%, EV={zone['ev']:.2f}%")

# Ordering proportions (Section C)
props = result.section_c['long']['proportions']
print(f"MFE-first: {props['mfe_first']*100:.1f}%")
print(f"MAE-first: {props['mae_first']*100:.1f}%")

Viewing Plots

# Show all plots
import matplotlib.pyplot as plt
plt.show()

# Show specific plot
result.plots['scatter'].show()

# Save plot
result.plots['heatmap_prob'].savefig('tp_sl_heatmap.png', dpi=150)

Configuration Options

Key AnalysisConfig parameters:

Parameter	Default	Description
H	10	Forward horizon in bars
sections	All	List of sections to run: ['A','B','C','D','E','F']
trim_method	'iqr'	Outlier removal: 'iqr', 'percentile', or None
trim_k	1.5	IQR multiplier (3.0 = very permissive)
store_paths	True	Store full paths (required for Section D)
vol_col	None	Volatility column name (for Section E)
n_clusters	None	Number of clusters (auto if None)

Example Configurations

Fast analysis (just geometry and frontiers):

config = AnalysisConfig(
    H=10,
    sections=['A', 'B'],
    store_paths=False,  # Faster
)

Full analysis with custom settings:

config = AnalysisConfig(
    H=30,                    # 30-bar horizon
    sections=['A', 'B', 'C', 'D', 'E', 'F'],  # All sections
    trim_k=3.0,              # More permissive outlier removal
    store_paths=True,
    vol_col='atr_14',        # Use 14-period ATR
    n_regimes=3,             # Split into 3 volatility regimes
)

Next Steps

Now that you've run your first analysis:

1. Understand the concepts: Core Concepts
2. Explore each section: User Guide
3. See real examples: Examples
4. Dive into the API: API Reference