Backtesting — Full Backtesting Skill

This skill implements the full 5-stage backtesting methodology from the course material: Data → Research → Metrics → Parameterisation → Validation. It provides:

• 30+ risk/performance ratios (flat, numpy-vectorized, no classes)
• 10 classes of indicators following the course taxonomy (trend-following, oscillators, contrarians, flow, combined, discrete counts, seasonality, statistical, referential, fundamental)
• Event-driven backtesting engine with 8 built-in strategies
• Forward-looking simulation (Johnson SU marginals + t/Gaussian copula)
• Portfolio theory (Markowitz efficient frontier, portfolio-of-portfolios)
• Walk-forward cross-validation with IS/OOS split + gap
• Stress testing with parametric scenario shocks
• Fundamental analysis (Altman Z, Piotroski F, DuPont)

All scripts use only numpy, pandas, and scipy. No heavy dependencies.

Part of the Gauss314 Skills Repository.

File Map

skills/backtesting/
├── SKILL.md                         ← This file
├── references/
│   ├── BACKTESTING_THEORY.md        ← Marco conceptual: GIGO, trilema, 5 etapas (ES)
│   ├── RATIOS.md                    ← Fórmulas, convención de retornos, advertencias (ES)
│   ├── FEATURES.md                  ← Taxonomía de 10 clases de indicadores con edges (ES)
│   ├── SIMULATIONS.md               ← Pipeline Johnson SU + cópula (ES)
│   ├── VALIDATION.md                ← Suite de validación de 4 niveles (ES)
│   └── OTHER_FEATURES.md            ← Fundamental, Sentimiento, Exógenos (ES)
├── assets/
│   ├── sp500_returns.csv            ← SPY benchmark daily returns (lin + log), 1980-today
│   ├── momentum_sma50_200_returns.csv ← SMA(50)/SMA(200) crossover strategy returns
│   ├── contrarian_bbands_returns.csv  ← Bollinger Band contrarian strategy returns
│   ├── sample_portfolios.json       ← Real investor portfolios (Buffett, Dalio, Ackman, 60/40)
│   ├── defaults.json                ← Default parameters (VaR alpha, windows, etc.)
│   └── validation_cases.json        ← 6 known cases for ratio validation
├── scripts/
│   ├── __init__.py
│   ├── ratios.py                    ← 30+ flat numpy functions for all risk/performance ratios
│   ├── indicators.py                ← 10 classes of technical/statistical/fundamental indicators
│   ├── engine.py                    ← Event-driven BacktestEngine with 8 built-in strategies
│   ├── backtesting.py               ← CLI: run, sweep, walkforward, montecarlo, optmpt, event, validate
│   ├── simulations.py               ← CLI: marginal, copula, run, portfolio, scenarios
│   ├── forward.py                   ← CLI: project, risk, stress, summary
│   ├── distributions.py             ← Fit + KS test for Normal/t/NCt/Laplace/JohnsonSU
│   ├── copulas.py                   ← t/Gaussian/Clayton/Gumbel/Frank copulas + sampling
│   ├── fundamental_ratios.py        ← Income/balance/cashflow metrics, DuPont, Altman Z, Piotroski
│   └── validate.py                  ← 4-level validation: CLI modes, math consistency, edge cases, regression
└── tests/
    └── test_ratios.py               ← 18 pytest tests for core ratios

What each file does

Quick Start

Basic Ratios

# Compute all 30+ ratios on a CSV of prices
py scripts/backtesting.py run --prices assets/sp500_returns.csv

# Compute with benchmark comparison
py scripts/backtesting.py run --prices assets/momentum_sma50_200_returns.csv --benchmark assets/sp500_returns.csv

Validate (4-level suite)

# Full validation (33 checks across 4 levels)
py scripts/validate.py

# Single level
py scripts/validate.py --nivel 1

Validates CLI modes, mathematical consistency of all ratios, edge case resilience, and post-fix regression. See [references/VALIDATION.md](./references/VALIDATION.md) for the detailed breakdown of all 33 checks.

Event-Driven Backtest

# Load a CSV with OHLCV data and run SMA crossover
py scripts/backtesting.py event --data my_stock.csv --strategy sma_crossover --fast 50 --slow 200 --commission 0.001

Parameter Sweep

# 2D sweep over fast/slow MA windows
py scripts/backtesting.py sweep --prices assets/sp500_returns.csv --p1-min 10 --p1-max 100 --p1-step 10
# 2D over 2 parameters
py scripts/backtesting.py sweep --prices assets/sp500_returns.csv --p1-min 10 --p1-max 50 --p1-step 5 --p2-min 25 --p2-max 200 --p2-step 25

Walk-Forward

py scripts/backtesting.py walkforward --prices assets/sp500_returns.csv --splits 5 --gap 21

Markowitz Optimization

py scripts/backtesting.py optmpt --assets assets/sp500_returns.csv --iterations 5000

Forward Simulation

py scripts/simulations.py marginal --returns assets/sp500_returns.csv
py scripts/simulations.py copula --returns assets/sp500_returns.csv --df 4
py scripts/forward.py project --returns assets/sp500_returns.csv --horizon 252 --paths 10000 --drift 0.08
py scripts/forward.py risk --returns assets/sp500_returns.csv --horizon 252 --paths 10000

Portfolio Simulation

py scripts/simulations.py portfolio --name warren_buffett
py scripts/simulations.py scenarios --name warren_buffett --cagr -0.3,-0.15,0,0.2,0.35,0.5

Using Ratios as a Library

from scripts.ratios import *

prices = np.array([100, 105, 102, 110, 108, 115])
r = linear_returns(prices)    # [0.05, -0.0286, 0.0784, -0.0182, 0.0648]
lr = log_returns(prices)      # [0.0488, -0.0290, 0.0755, -0.0183, 0.0628]

sharpe_ratio(r)               # 0.847
max_drawdown(prices)          # -0.0370
kelly_fraction(lr)            # 0.0793
var_all(r, alpha=0.05)        # {'empirical': ..., 'normal': ..., 'johnsonsu': ...}
profit_factor(lr)             # 2.314
payoff_ratio(lr)              # 1.578
rachev_c(lr, alpha=0.05)      # 1.234
common_sense_ratio(lr)        # 2.856

Using the Engine

from scripts.engine import BacktestEngine

eng = BacktestEngine(initial_capital=1.0, commission=0.001, slippage=0.0005)
eng.load_data(df_ohlcv)
result = eng.run(strategy='sma_crossover', strategy_params={'fast': 50, 'slow': 200})

print(result['metrics']['sharpe_ratio'])     # 0.847
print(result['trades'])
print(result['metrics'])

Dependencies

To run the full validation suite (py scripts/validate.py) you also need pytest for the Level 4 regression check.

No arch, quantlib, sklearn required.

See Also

• Gauss314 Skills Repository — other skills for financial data
• references/BACKTESTING_THEORY.md — marco conceptual del backtesting (ES)
• references/RATIOS.md — fórmulas, convención de retornos, advertencias (ES)
• references/FEATURES.md — taxonomía de 10 clases de indicadores con edges (ES)
• references/SIMULATIONS.md — pipeline de Johnson SU + cópula (ES)
• references/VALIDATION.md — suite de validación de 4 niveles (ES)
• references/OTHER_FEATURES.md — fundamental, sentimiento, exógenos (ES)