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: > 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:

Strategy Hypothesis Formulation

Before fetching data, clarify the strategy with the user:

1. Universe: Which stocks? (Single stock, S&P 500, sector, custom list) 2. Signal: What is the entry trigger? (Moving average crossover, RSI level, fundamental metric, etc.) 3. Entry: Long, Short, or Long/Short? 4. Exit: Trailing stop, fixed target, time-based, signal reversal? 5. Holding Period: Daily, weekly, monthly? 6. Position Sizing: Equal weight, volatility-adjusted, Kelly? 7. 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

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

Analysis Workflow

Step 1 — Data Preparation

1. Align all price series to the same trading calendar (drop non-overlapping dates). 2. Use adjustedClose for all calculations (accounts for splits and dividends). 3. Compute daily returns: r_t = (adjustedClose_t / adjustedClose_{t-1}) - 1 4. 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:

Step 6 — Walk-Forward Validation

To detect overfitting, split the full history:

Walk-Forward Efficiency Ratio:

WFE = OOS_Sharpe / IS_Sharpe

Step 7 — Risk Scenarios

Stress-test the strategy in 3 regimes: 1. 2020 COVID Crash: Feb 19 – Mar 23, 2020 (S&P -34% in 33 days) 2. 2022 Bear Market: Jan 1 – Dec 31, 2022 (S&P -19.4%) 3. 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

API Key required — Register at https://finskills.net to get your free key. Header: X-API-Key: > Get your API key: Register at https://finskills.net — free tier available, Pro plan unlocks real-time quotes, history, and financials.