backtest-expert

Data & APIs

Design, execute, and evaluate quantitative trading strategies using historical price data and Fama-French factor attribution via the Finskills API.

Install

openclaw skills install finskills-backtest-expert

Backtest Expert

Design, execute, and evaluate quantitative trading strategies using historical price data from the Finskills API. Transforms a natural-language strategy hypothesis into a rigorous backtest with performance metrics, drawdown analysis, Fama-French factor attribution, and walk-forward validation.

Setup

API Key required — Register at https://finskills.net to get your free key.
Header: X-API-Key: <your_api_key>

Get your API key: Register at https://finskills.net — free tier available, Pro plan unlocks real-time quotes, history, and financials.

When to Activate This Skill

Activate when the user:

Describes a trading rule they want to test historically
Asks "how would X strategy have performed?"
Wants to validate a momentum, mean-reversion, or factor-based strategy
Asks about Sharpe ratio, maximum drawdown, or strategy performance metrics
Wants to see walk-forward or out-of-sample testing
Asks to compare two or more strategies head-to-head

Strategy Hypothesis Formulation

Before fetching data, clarify the strategy with the user:

Universe: Which stocks? (Single stock, S&P 500, sector, custom list)
Signal: What is the entry trigger? (Moving average crossover, RSI level, fundamental metric, etc.)
Entry: Long, Short, or Long/Short?
Exit: Trailing stop, fixed target, time-based, signal reversal?
Holding Period: Daily, weekly, monthly?
Position Sizing: Equal weight, volatility-adjusted, Kelly?
Benchmark: SPY (default) or sector ETF?

State the strategy in a formal grammar:

ENTRY:  Buy {SYMBOL} when {condition}
EXIT:   Sell when {condition} OR stop at {-X%}
HOLD:   Max {N} days / bars
SIZING: {$N fixed / X% of portfolio / equal weight}
BENCH:  {SPY / QQQ / custom}
PERIOD: From {YYYY-MM-DD} to {YYYY-MM-DD}

Data Retrieval — Finskills API Calls

1. Historical OHLCV for Strategy Symbols

GET https://finskills.net/v1/stocks/history/{SYMBOL}?period=5y&interval=1d

Parameters:

period: 1y, 2y, 5y, 10y, max
interval: 1d (daily), 1wk (weekly), 1mo (monthly)

Extract: date, open, high, low, close, volume, adjustedClose

Repeat for each symbol in the strategy universe AND the benchmark (SPY or QQQ).

2. Fama-French 3-Factor Data

GET https://finskills.net/v1/free/market/fama-french

Extract: Mkt-RF (market excess return), SMB (small-minus-big), HML (high-minus-low), RF (risk-free rate)
Use for: Attribution analysis — what % of strategy return is explained by market, size, value factors

Analysis Workflow

Step 1 — Data Preparation

Align all price series to the same trading calendar (drop non-overlapping dates).
Use adjustedClose for all calculations (accounts for splits and dividends).
Compute daily returns: r_t = (adjustedClose_t / adjustedClose_{t-1}) - 1
Handle missing data: forward-fill up to 2 days; drop if missing > 2 consecutive days.

Step 2 — Signal Generation

Based on the strategy rules, generate entry/exit signals for each day in the backtest.

Example Signal Implementations:

Simple Moving Average Crossover:

SMA_fast = rolling_mean(adjustedClose, window=fast_n)
SMA_slow = rolling_mean(adjustedClose, window=slow_n)
Signal = 1 (Long)  when SMA_fast crosses above SMA_slow
Signal = 0 (Flat)  when SMA_fast crosses below SMA_slow

RSI Reversion:

RSI = 100 - 100 / (1 + avg_gain_14 / avg_loss_14)
Signal = 1 (Long)  when RSI < 30 (oversold)
Signal = 0 (Flat)  when RSI > 70 (overbought)

Momentum:

Momentum_N = current_price / price_N_days_ago - 1
Signal = 1 when Momentum_N > threshold
Signal = 0 otherwise

Step 3 — Portfolio Returns Calculation

strategy_return_t = Signal_{t-1} × return_t   (signal from prior day, no lookahead)
benchmark_return_t = SPY daily return

Compute cumulative returns:

cumulative_return = PRODUCT(1 + strategy_return_t) - 1

Apply transaction costs (default: 0.10% per round-trip trade, adjustable):

cost_t = 0.001 × |Signal_t - Signal_{t-1}|  (cost only on signal changes)
net_return_t = strategy_return_t - cost_t

Step 4 — Performance Metrics

Compute the full performance scorecard:

Metric	Formula
Total Return	PRODUCT(1 + r_t) - 1
Annualized Return (CAGR)	(1 + total_return)^(252/n_days) - 1
Annualized Volatility	STD(r_t) × √252
Sharpe Ratio	(CAGR - rf_rate) / annualized_vol
Sortino Ratio	(CAGR - rf_rate) / downside_vol
Max Drawdown	max(1 - V_t / max(V_s for s ≤ t))
Calmar Ratio	CAGR / Max Drawdown
Win Rate	% of trading days with positive return
Avg Win / Avg Loss	mean positive return / mean negative return
Profit Factor	sum wins / sum losses
Alpha (vs benchmark)	CAGR_strategy - CAGR_benchmark
Beta	COV(r_strategy, r_benchmark) / VAR(r_benchmark)
Information Ratio	Alpha / tracking error
Number of Trades	Count of signal transitions
Avg Holding Period	Avg days per trade

Step 5 — Fama-French Factor Attribution

Regress strategy excess returns against F-F factors:

r_strategy - RF = α + β₁(Mkt-RF) + β₂(SMB) + β₃(HML) + ε

Interpret:

α (Alpha): Risk-adjusted return not explained by factors — the "skill" component
β₁ (Market Beta): Sensitivity to market direction
β₂ (SMB loading): Small-cap vs. large-cap tilt
β₃ (HML loading): Value (high) vs. growth (low) tilt
R²: How much of returns are explained by common factors (R² < 0.3 = distinctive strategy)

Step 6 — Walk-Forward Validation

To detect overfitting, split the full history:

In-Sample (IS): First 60% of data — used to identify strategy parameters
Out-of-Sample (OOS): Last 40% — held out, tested after IS optimization

Walk-Forward Efficiency Ratio:

WFE = OOS_Sharpe / IS_Sharpe

WFE > 0.7: Strategy generalizes well — low overfitting concern
WFE 0.4–0.7: Some degradation — monitor live, reduce leverage
WFE < 0.4: Likely overfit to historical data — do not trade with confidence

Step 7 — Risk Scenarios

Stress-test the strategy in 3 regimes:

2020 COVID Crash: Feb 19 – Mar 23, 2020 (S&P -34% in 33 days)
2022 Bear Market: Jan 1 – Dec 31, 2022 (S&P -19.4%)
2008 Financial Crisis: Oct 1 – Dec 31, 2008 (S&P -38% in quarter)

For each stress scenario, report the strategy's return vs. SPY return.

Output Format

╔══════════════════════════════════════════════════════════════════╗
║    BACKTEST REPORT  —  {STRATEGY NAME}                          ║
║    Period: {start_date}  to  {end_date}   ({N} trading days)   ║
╚══════════════════════════════════════════════════════════════════╝

📋 STRATEGY SUMMARY
  Universe:       {SYMBOL(s) or index}
  Entry Signal:   {description}
  Exit Signal:    {description}
  Position Type:  {Long / Long-Short}
  Benchmark:      {SPY / QQQ}
  Tx Cost:        0.10% per round-trip

📈 CUMULATIVE RETURNS
  Strategy:   +{X.X}%  ($100K → ${value}K)
  Benchmark:  +{X.X}%  ($100K → ${value}K)
  Alpha:      +{X.X}% annualized

📊 PERFORMANCE SCORECARD
               Strategy     Benchmark    Δ vs. Bench
  CAGR:         {X.X}%       {X.X}%      +{X.X}%
  Volatility:   {X.X}%       {X.X}%      
  Sharpe:        {X.XX}       {X.XX}      +{X.XX}
  Sortino:       {X.XX}       {X.XX}
  Max Drawdown: -{X.X}%      -{X.X}%
  Calmar:        {X.XX}       {X.XX}
  Win Rate:      {X.X}%       —
  Profit Factor: {X.XX}       —
  Avg Hold:      {N} days     —
  N Trades:      {count}      —

📉 DRAWDOWN ANALYSIS
  Worst Drawdown:       -{X}%  (from {date} to {date}, {N} days to recover)
  2nd Worst Drawdown:   -{X}%
  Average Drawdown:     -{X}%

🧬 FACTOR ATTRIBUTION (Fama-French)
  α (Annualized):   {X.XX}%  [{statistically significant yes/no}]
  β Market:         {X.XX}
  β SMB (size):     {X.XX}  [{small-cap / large-cap} tilt]
  β HML (value):    {X.XX}  [{value / growth} tilt]
  R² (explained):   {X}%

🔁 WALK-FORWARD VALIDATION
  In-Sample  ({dates}):  Sharpe {X.XX},  CAGR {X.X}%
  Out-of-Sample ({dates}): Sharpe {X.XX},  CAGR {X.X}%
  WF Efficiency Ratio:   {X.XX}  → {Generalizes well / Some degradation / Likely overfit}

💥 STRESS TEST
  2020 COVID Crash:    Strategy {+/-X}%  vs SPY -{X}%
  2022 Bear Market:    Strategy {+/-X}%  vs SPY -{X}%
  2008 Q4 Crash:       Strategy {+/-X}%  vs SPY -{X}%

🎯 VERDICT
  Signal Quality:   {Strong / Moderate / Weak / Overfit}
  Trade Live?       {Yes / Caution / No — further development needed}
  Suggested Refinement: {1–2 concrete suggestions to improve the strategy}

Limitations

Backtests suffer from survivorship bias (S&P 500 historical data excludes delisted companies).
Look-ahead bias is explicitly prevented by using prior-day signals; verify signal construction.
Slippage is not modeled (real-world execution will have impact beyond transaction cost assumption).
Fama-French data may have a 1–2 month lag; factors are smoothed — not for very recent periods.
Past performance in backtests does not guarantee future results.