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
| File | Role | Key Functions / Modes |
|---|---|---|
ratios.py | The core library. Every ratio is a flat function accepting 1-D arrays. | sharpe_ratio, max_drawdown, var_all, cvar_all, kelly_fraction, payoff_ratio, profit_factor, rachev_a/b/c, common_sense_ratio, ruin_curve, compute_all |
indicators.py | 10 classes of indicators, covering all types from the course taxonomy. | rsi, adx, bbands, macd, atr, cross_indicator, range_bound, zscore_norm, poisson_rate, binomial_ratio, fourier_terms, best_fit_dist |
engine.py | BacktestEngine class and 8 strategy functions. | BacktestEngine, strategy_sma_crossover, strategy_rsi_cross, strategy_bbands_contrarian, strategy_growth_momentum_combo |
backtesting.py | Main CLI. Run full backtests, walks, sweeps, optimization. | run, sweep, walkforward, montecarlo, optmpt, event, validate, bench |
simulations.py | Forward-looking simulation with Johnson SU + copula. | marginal, copula, run, portfolio, scenarios |
forward.py | Risk projection and stress testing. | project, risk, stress, summary |
distributions.py | Distribution fitting and comparison. | fit, best_fit, compare_distributions, sample |
copulas.py | Copula fitting and sampling. | fit_t, fit_gaussian, sample_t, sample_gaussian, validate_copula |
fundamental_ratios.py | Fundamental analysis ratios. | income_metrics, valuation_metrics, dupont, altman_z, piotroski |
validate.py | 4-level integration testing suite. | 33 checks across CLI, math, edge cases, regression |
Quick Start
Basic Ratios
bash# 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)
bash# 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 for the detailed breakdown of all 33 checks.
Event-Driven Backtest
bash# 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
bash# 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
bashpy scripts/backtesting.py walkforward --prices assets/sp500_returns.csv --splits 5 --gap 21
Markowitz Optimization
bashpy scripts/backtesting.py optmpt --assets assets/sp500_returns.csv --iterations 5000
Forward Simulation
bashpy 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
bashpy 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
pythonfrom 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
pythonfrom 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
| Library | Required | Used for |
|---|---|---|
numpy | ✅ | Vectorised computation, arrays, cumprod |
pandas | ✅ | CSV I/O, rolling operations, DataFrames |
scipy.stats | ✅ | Distribution fitting, KS test, copulas |
statsmodels | Optional | STL decomposition in indicators.py (Class 5) |
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)
