Backtest Expert

Overview

position-sizer and drawdown-circuit-breaker manage risk once you've already decided to trade a rule — neither tells you whether the rule itself actually works. This skill fills that gap: given historical price data and a set of buy/sell signals (either supplied directly or generated from a simple moving-average crossover), it simulates the trades a long-only, one-position-at-a-time strategy would have taken and reports the standard backtest metrics.

Not financial advice. A backtest describes how a rule performed on one specific historical sample. It does not predict future performance, and a rule tuned by trial-and-error against the same data it's being "backtested" on will look better than it is (overfitting/curve-fitting) — see Known limitations.

When to use

• ทดสอบกฎเทรดกับข้อมูลราคาย้อนหลัง ก่อนใช้เงินจริง
• Evaluating a simple moving-average crossover (or any buy/sell signal series you already have) against historical prices
• Comparing win rate, average return, max drawdown, and profit factor across a few rule variants on the same data

When NOT to use

• Sizing or risk-gating a specific upcoming trade — that's position-sizer/drawdown-circuit-breaker, not this skill
• The user wants live/real-time strategy execution — this is historical simulation only, it does not place or track live trades
• Short selling, multi-position, or portfolio-level backtesting — this engine is long-only and one-position-at-a-time; a strategy needing more than that needs a different tool

Core knowledge

Trade simulation: long-only, one position at a time. Enters on a "buy" signal only while flat; exits on a "sell" signal only while in a position. A "buy" while already in a position, or a "sell" while flat, is a no-op — it does not pyramid into a second position or close a position that doesn't exist.

Metrics reported:

An open position at the end of the data (a "buy" with no later "sell") is not counted as a completed trade and is flagged separately (open_position_at_end) — don't silently drop it from the report.

Moving-average crossover signal generator: a simple, common way to produce buy/sell signals mechanically instead of supplying them by hand. fast_period/slow_period are trailing-window sizes (in bars); a "buy" fires the first bar the fast MA moves above the slow MA, "sell" the first bar it moves below. No lookahead — each bar's MA only uses that bar and earlier ones. Bars without enough history for both MAs get "hold".

Common mistakes

1. Tuning the fast/slow period (or any rule parameter) by trying many values against the same historical sample and keeping whichever looks best — that's curve-fitting, not validation; the resulting metrics describe how well the rule was fit to this data, not how it will perform on new data.
2. Treating a high win rate as good on its own — a rule with a 70% win rate and rare large losses can still have a negative profit factor; report win rate, average return, and profit factor together, not in isolation.
3. Ignoring open_position_at_end — a trade still open when the data ends isn't a loss or a win, it's incomplete; don't fold it into win rate as either.
4. Applying this long-only, single-position engine to a strategy that actually shorts, pyramids, or holds multiple concurrent positions — the metrics would silently misrepresent a strategy this engine can't actually simulate.
5. Not accounting for what this backtest excludes: no commissions, slippage, taxes, or bid-ask spread by default — real returns will be lower than a raw backtest suggests, especially for a rule with many trades.

Code

backtest.py — Decimal arithmetic, no dependencies:

• run_backtest(bars) → BacktestResult(trades, total_trades, win_rate, avg_return_pct, total_return_pct, max_drawdown_pct, profit_factor, open_position_at_end), where bars is a chronological list of {"date", "price", "signal"}
• generate_ma_crossover_signals(prices, fast_period, slow_period) → the same list with a "signal" key added, ready to feed into run_backtest

Run python3 backtest.py for the self-test (includes a fully hand-verified trade sequence and a hand-verified MA-crossover signal sequence, not just round-trip assertions).

Known limitations

• No commissions, slippage, spread, taxes, shorting, position sizing, or multi-position support — this is a minimal long-only single-position simulator, not a full trading-system backtester.
• Says nothing about statistical significance — a handful of trades on one historical window is a small sample; treat a backtest over a short period or few trades as weak evidence, not proof a rule works.
• Overfitting is a real, easy-to-fall-into trap: if the rule's parameters were chosen by testing many variants against this same data, the reported metrics are optimistic. A rule validated on data it was never tuned against (out-of-sample / walk-forward testing) is much stronger evidence — this skill does not enforce that discipline for you.
• Past performance shown by any backtest, however carefully done, does not guarantee future results.