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Chinese

Backtesting — Full Backtesting Skill

回测 — 完整回测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.
本Skill实现了课程资料中的完整五阶段回测方法论：数据→研究→指标→参数化→验证。它提供以下功能：
  • 30+种风险/绩效比率（扁平化、numpy向量化实现，无类结构）
  • 10类指标，遵循课程分类体系（趋势跟踪型、震荡型、逆向型、资金流型、组合型、离散计数型、季节性型、统计型、参考型、基本面型）
  • 事件驱动回测引擎，内置8种策略
  • 前瞻性模拟（Johnson SU边缘分布 + t/高斯Copula）
  • 投资组合理论（Markowitz有效前沿、组合的组合）
  • 滚动交叉验证（walk-forward CV），包含样本内/样本外拆分与间隔
  • 压力测试，支持参数化场景冲击
  • 基本面分析（Altman Z、Piotroski F、杜邦分析）
所有脚本仅使用
numpy
、
pandas
和
scipy
，无重型依赖。
属于Gauss314 Skills Repository的一部分。

File Map

文件结构

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
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

各文件功能

FileRoleKey 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
文件作用核心函数/模式
ratios.py
核心库。每个比率都是接受一维数组的扁平化函数。
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类指标，覆盖课程分类体系中的所有类型。
rsi
, 
adx
, 
bbands
, 
macd
, 
atr
, 
cross_indicator
, 
range_bound
, 
zscore_norm
, 
poisson_rate
, 
binomial_ratio
, 
fourier_terms
, 
best_fit_dist
engine.py
BacktestEngine类与8种策略函数。
BacktestEngine
, 
strategy_sma_crossover
, 
strategy_rsi_cross
, 
strategy_bbands_contrarian
, 
strategy_growth_momentum_combo
backtesting.py
主CLI工具。运行完整回测、滚动验证、参数扫描、优化等。
run
, 
sweep
, 
walkforward
, 
montecarlo
, 
optmpt
, 
event
, 
validate
, 
bench
simulations.py
基于Johnson SU + Copula的前瞻性模拟工具。
marginal
, 
copula
, 
run
, 
portfolio
, 
scenarios
forward.py
风险预测与压力测试工具。
project
, 
risk
, 
stress
, 
summary
distributions.py
分布拟合与对比工具。
fit
, 
best_fit
, 
compare_distributions
, 
sample
copulas.py
Copula拟合与采样工具。
fit_t
, 
fit_gaussian
, 
sample_t
, 
sample_gaussian
, 
validate_copula
fundamental_ratios.py
基本面分析比率工具。
income_metrics
, 
valuation_metrics
, 
dupont
, 
altman_z
, 
piotroski
validate.py
四级集成测试套件。覆盖CLI、数学逻辑、边缘场景、回归的33项检查

Quick Start

快速开始

Basic Ratios

基础比率计算

bash
undefined
bash
undefined

Compute all 30+ ratios on a CSV of prices

Compute all 30+ ratios on a CSV of prices

py scripts/backtesting.py run --prices assets/sp500_returns.csv
py scripts/backtesting.py run --prices assets/sp500_returns.csv

Compute with benchmark comparison

Compute with benchmark comparison

py scripts/backtesting.py run --prices assets/momentum_sma50_200_returns.csv --benchmark assets/sp500_returns.csv
undefined
py scripts/backtesting.py run --prices assets/momentum_sma50_200_returns.csv --benchmark assets/sp500_returns.csv
undefined

Validate (4-level suite)

验证（四级测试套件）

bash
undefined
bash
undefined

Full validation (33 checks across 4 levels)

Full validation (33 checks across 4 levels)

py scripts/validate.py
py scripts/validate.py

Single level

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.
py scripts/validate.py --nivel 1

验证CLI模式、所有比率的数学一致性、边缘场景适应性以及修复后回归情况。查看[`references/VALIDATION.md`](./references/VALIDATION.md)了解33项检查的详细细分。

Event-Driven Backtest

事件驱动回测

bash
undefined
bash
undefined

Load a CSV with OHLCV data and run SMA crossover

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
undefined
py scripts/backtesting.py event --data my_stock.csv --strategy sma_crossover --fast 50 --slow 200 --commission 0.001
undefined

Parameter Sweep

参数扫描

bash
undefined
bash
undefined

2D sweep over fast/slow MA windows

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
py scripts/backtesting.py sweep --prices assets/sp500_returns.csv --p1-min 10 --p1-max 100 --p1-step 10

2D over 2 parameters

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
undefined
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
undefined

Walk-Forward

滚动交叉验证

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

Markowitz Optimization

Markowitz优化

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

Forward Simulation

前瞻性模拟

bash
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
bash
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

投资组合模拟

bash
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
bash
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

将比率库用作模块

python
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
python
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

使用回测引擎

python
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'])
python
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

依赖项

LibraryRequiredUsed for
numpy
✅Vectorised computation, arrays, cumprod
pandas
✅CSV I/O, rolling operations, DataFrames
scipy.stats
✅Distribution fitting, KS test, copulas
statsmodels
OptionalSTL 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.
库是否必需用途
numpy
✅向量化计算、数组、累积乘积
pandas
✅CSV输入输出、滚动操作、DataFrames
scipy.stats
✅分布拟合、KS检验、Copula
statsmodels
可选indicators.py中的STL分解（第5类指标）
要运行完整的验证套件（
py scripts/validate.py
），还需要
pytest
来进行第4级回归检查。
无需
arch
、
quantlib
、
sklearn
。

See Also

相关链接

  • 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)
  • Gauss314 Skills Repository — 其他金融数据相关Skill
  • references/BACKTESTING_THEORY.md
    — 回测宏观概念（西班牙语）
  • references/RATIOS.md
    — 公式、收益率约定、注意事项（西班牙语）
  • references/FEATURES.md
    — 带优势的10类指标分类体系（西班牙语）
  • references/SIMULATIONS.md
    — Johnson SU + Copula流程（西班牙语）
  • references/VALIDATION.md
    — 四级验证套件（西班牙语）
  • references/OTHER_FEATURES.md
    — 基本面、情绪、外生变量（西班牙语）