/backtesting

name: backtesting
description: "Academic backtesting framework for quantitative research. ~30 risk and performance ratios, 10 classes of indicators, event-driven engine with 6+ strategies, MPT optimizer, forward-looking simulation with Johnson SU + t-Copula, walk-forward CV, stress testing, fundamental analysis (Altman Z, Piotroski, DuPont). All flat Python + numpy."
license: MIT

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](https://github.com/gauss314/skills).

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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 |

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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-Driv