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# 🧿 Backtest Kit

> A TypeScript engine for backtesting **and** live-trading strategies — crypto, forex, DEX, spot or futures — where the code you test is the code you ship. See [reference implementation](https://github.com/tripolskypetr/backtest-kit/tree/master/example)

![screenshot](https://raw.githubusercontent.com/tripolskypetr/backtest-kit/HEAD/assets/screenshots/screenshot16.png)

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Most trading bots don't die because the strategy was wrong. They die because the backtest quietly read tomorrow's candle, because the process crashed mid-fill and opened the position twice, because the exchange rejected an order and the bot kept trading a ghost. The strategy was never the hard part — the *plumbing* was.

`backtest-kit` is that plumbing, closed off one failure at a time over a year of live trading and running real money in production at [TheOneTrade](https://theonetrade.github.io). This page walks the failures that kill bots and shows how each one is designed out of the default path — not "discouraged," not "documented," but structurally unavailable unless you go out of your way to defeat the engine. Every claim opens into **The Code / The Math / The Proof** so you (or the model reading this for you) can check the work instead of trusting the pitch.

📚 **[API Reference](https://backtest-kit.github.io/documents/example_02_first_backtest.html)** · 🌟 **[Reference implementation](https://github.com/tripolskypetr/backtest-kit/tree/master/example)** · 📰 **[Article series](https://backtest-kit.github.io/documents/article_07_ai_news_trading_signals.html)**

---

## Start here

Three on-ramps, one engine. Casual keeps the boilerplate inside the CLI; Sidekick ejects every wire into your repo; Docker gives you a restart-safe box.

<details>
<summary>The Code</summary>

```bash
# Casual — your repo holds only strategy files; docs auto-fetched into docs/lib/
npx @backtest-kit/cli --init --output backtest-kit-project
cd backtest-kit-project && npm install && npm start

# Full control — exchange/frames/risk/runner all editable in your project
npx -y @backtest-kit/sidekick my-trading-bot && cd my-trading-bot && npm start

# Docker — zero-downtime live trading
npx @backtest-kit/cli --docker && cd backtest-kit-docker
MODE=live SYMBOL=TRXUSDT STRATEGY_FILE=./content/feb_2026/feb_2026.strategy.ts docker-compose up -d
```

A whole strategy is three registrations and a run call. No bootstrap, no DI container to learn:

```typescript
import ccxt from 'ccxt';
import { addExchangeSchema, addStrategySchema, addFrameSchema, Position,
         Backtest, listenSignalBacktest, listenDoneBacktest } from 'backtest-kit';

addExchangeSchema({
  exchangeName: 'binance',
  getCandles: async (symbol, interval, since, limit) => {
    const ex = new ccxt.binance();
    const ohlcv = await ex.fetchOHLCV(symbol, interval, since.getTime(), limit);
    return ohlcv.map(([timestamp, open, high, low, close, volume]) =>
      ({ timestamp, open, high, low, close, volume }));
  },
  formatPrice: (s, p) => p.toFixed(2), formatQuantity: (s, q) => q.toFixed(8),
});

addFrameSchema({ frameName: 'feb-2026', interval: '1m',
  startDate: new Date('2026-02-01'), endDate: new Date('2026-02-28') });

addStrategySchema({
  strategyName: 'my-strategy', interval: '15m',
  getSignal: async (symbol, when, currentPrice) => ({
    position: 'long',
    ...Position.bracket({ position: 'long', currentPrice, percentTakeProfit: 2, percentStopLoss: 1 }),
    minuteEstimatedTime: 60 * 24, cost: 100,
  }),
});

Backtest.background('BTCUSDT', { strategyName: 'my-strategy', exchangeName: 'binance', frameName: 'feb-2026' });
listenSignalBacktest(console.log);
listenDoneBacktest(async (e) => { await Backtest.dump(e.symbol, e.strategyName); });
```

</details>

---

## The rakes — and where they went

What follows isn't a feature list. It's the set of mistakes that quietly drain accounts, each one paired with the design decision that took it off the table. If you've shipped a bot before, you've stepped on at least three of these.

### 1. Your backtest lied to you, and you'll only find out with real money

Look-ahead bias is the assassin of algo trading: a single line that touches a future candle, an indicator loaded without a timestamp filter, one forgotten `<=`. The backtest prints a beautiful equity curve that can *never* be reproduced live, and you deploy straight into a drawdown.

The usual defense is "be careful." Careful doesn't survive a 2,000-line strategy or a refactor at 1 a.m. So the cure here isn't discipline — it's removal of the failure surface. There is no timestamp parameter to forget. An ambient temporal context flows through every async call via Node's `AsyncLocalStorage`, and the data layer physically refuses to hand you a candle past "now." The pending (still-forming) candle is never returned, because its half-finished OHLC would poison every indicator.

The one rule this rests on: that context is live for the whole `await` chain of your `getSignal` and every `listen*` callback — including across `Promise.all`, which is where strategy code actually runs. It is not sorcery over execution you deliberately detach from that chain. A bare timer, an `EventEmitter`, a forked process, or the web dashboard reads engine state by **identifier** (signal id / symbol), not by inheriting the ambient clock — that explicit, id-based interop is exactly how the frontend talks to a running backtest. Inside the hooks the guarantee holds; step outside them on purpose and you address the engine deliberately rather than by accident.

<details>
<summary>The Math</summary>

Every request resolves "now" from the ambient context, aligns down to the interval boundary, and treats the pending candle as exclusive:

```
when        = current execution-context time   (AsyncLocalStorage)
stepMs      = interval duration                (1m → 60000)
alignedWhen = Math.floor(when / stepMs) * stepMs          // round down to boundary
since       = alignedWhen − limit * stepMs               // go back `limit` candles
```

- `since` is **inclusive** — first candle has `timestamp === since`.
- `alignedWhen` is **exclusive** — the candle covering `[alignedWhen, alignedWhen+stepMs)` is still open and is never returned.
- Range is the half-open `[since, alignedWhen)`; exactly `limit` candles return; timestamps are `since + i·stepMs`.

`getNextCandles()` is backtest-only and **throws in live mode** — there is no future to look at when "now" is wall-clock. `getRawCandles(limit?, sDate?, eDate?)` supports flexible windows, all clamped to `eDate ≤ when`. Order books and aggregated trades use the same alignment (trades always to a 1-minute boundary). All boundaries are **UTC**: a 4h candle aligns to `00/04/08/12/16/20 UTC` regardless of your local offset — so `since` values that look "uneven" in local time are exact in UTC. Because `since` is derived from the ambient `when`, multi-timeframe pulls inside one `getSignal` are automatically synchronized, and runtime and the persistent cache compute identical keys — deterministic, exact-timestamp retrieval.

</details>

<details>
<summary>The Code</summary>

```typescript
getSignal: async (symbol) => {
  // No timestamps anywhere. Context flows even through Promise.all —
  // all four timeframes are pinned to the same tick automatically.
  const [c1h, c15m, c5m, c1m] = await Promise.all([
    getCandles(symbol, '1h', 24),
    getCandles(symbol, '15m', 48),
    getCandles(symbol, '5m', 60),
    getCandles(symbol, '1m', 60),
  ]);
}
```

The bias you can't introduce by hand is the bias you'll never debug in production.

</details>

### 2. "It worked in the backtest" means nothing if live runs different code

The standard path productionizes a strategy by rewriting it: the research notebook becomes a second, hand-built live system with its own order logic, its own bugs, its own divergence. Now you have two strategies that *look* identical and behave differently exactly when it matters.

Here there is one code path. The `getSignal` you backtested is the `getSignal` that trades. Backtest mode feeds it historical timestamps; live mode feeds it `Date.now()`. The business logic — entries, validation, scheduled activation, TP/SL/timeout, partial closes — is byte-for-byte the same in both. The only differences are infrastructural: where the data comes from, not what you do with it.

<details>
<summary>The Code</summary>

```typescript
// Backtest — a historical frame drives the clock
Backtest.background('BTCUSDT', { strategyName, exchangeName, frameName });

// Live — wall-clock drives the clock; the strategy file is untouched
Live.background('BTCUSDT', { strategyName, exchangeName });   // keys via .env
listenSignalLive(async (e) => { if (e.action === 'closed') await Live.dump(e.symbol, e.strategyName); });

// Paper — live prices, no real orders, identical path. Validate here before risking capital.
```

And one engine, two ways to consume it — pick by use case, not by capability:

```typescript
// Event-driven (production bots, monitoring)
Backtest.background('BTCUSDT', config);
listenSignalBacktest(e => {/* … */});

// Async iterator (research, scripts, LLM agents)
for await (const event of Backtest.run('BTCUSDT', config)) { /* signal | progress | done */ }
```

</details>

<details>
<summary>The Proof</summary>

This is the property the test suite exists to defend, and the line in the sand for the whole project: **business logic is 100% synchronous across backtest and live.** Signal validation is identical in both modes; immediate activation behaves identically; scheduled-signal logic is fully synchronized; TP / SL / timeout checks do not differ. The only divergence is infrastructural — how candles, order books, and time are sourced. `validation.test.mjs`, `backtest.test.mjs`, and `callbacks.test.mjs` pin this behavior; `event.test.mjs` pins the live path against the same expectations. If the two ever drift, a test goes red before you do.

</details>

### 3. The crash that opens your position twice

A bot updating a position when the process dies — OOM, deploy, power blip — usually wakes up to corrupted state: a half-opened position, a cost basis that's wrong, an exit that never registered. Recovery by hand is where money leaks.

Every state mutation is written atomically to disk *before* it counts as done (write-temp-then-rename), and on restart the engine reloads to the last consistent state. Live runs reload persisted signal state on every start, and `Live.background()` shuts down gracefully — it waits for open positions to reach `closed` before stopping, so a deploy never severs a live trade mid-flight.

<details>
<summary>The Proof</summary>

Recovery is structural, not a feature you remember to enable. `PersistBase` does atomic write-to-temp + rename, repairs corrupted files, and verifies integrity in `waitForInit()`. Fifteen per-domain `Persist*Instance` classes cover everything that can change: Signal, State, Session, Candle, Risk, Partial, Breakeven, Schedule, Recent, Notification, Log, Measure, Interval, Memory. Concrete scenarios that resolve cleanly:

- Process killed during order placement → internal state unchanged, retried next tick.
- Network failure during an exchange call → automatic retry on the next tick.
- Power loss during a save → recovery from the last atomic write.
- OOM → graceful shutdown with state preserved.

```typescript
listenSignalLive(async (event) => {
  if (event.action === 'closed') {
    await Live.dump(event.symbol, event.strategyName);   // atomic snapshot to disk
    await Partial.dump(event.symbol, event.strategyName);
  }
  if (event.action === 'scheduled' || event.action === 'cancelled') {
    await Schedule.dump(event.symbol, event.strategyName);
  }
});
```

</details>

### 4. The state that can't be corrupted because it can't be expressed

"Is this position closed?" is a question you should never have to ask at runtime. A signal here moves through a strict lifecycle — **idle → scheduled → opened → active → closed** — modeled with TypeScript discriminated unions. Reading a closed position's live PnL, or mutating an active trade as if it were idle, isn't a bug you catch in QA; it's a line that won't compile.

<details>
<summary>The Code</summary>

Each state exposes only the data that is meaningful in that state, so the wrong access never type-checks:

```typescript
listenSignal((event) => {
  switch (event.action) {
    case 'idle':      /* no signal — only monitoring fields exist */            break;
    case 'scheduled': /* waiting for entry price — has priceOpen, scheduledAt */ break;
    case 'opened':    /* just filled — entry data, no closeReason yet */         break;
    case 'active':    /* live position — pnl, peakProfit, maxDrawdown */         break;
    case 'closed':    /* exited — closeReason, final pnl; live fields gone */     break;
  }
});
```

Before any signal reaches the engine it passes a validation pipeline: TP/SL prices positive, relationship correct (`TP > entry > SL` long, inverse short), risk/reward ≥ your minimum, timestamps not in the future, interval-throttling respected. Invalid signals are rejected or logged — never executed.

</details>

<details>
<summary>The Proof</summary>

The discriminated-union result types (`IStrategyTickResultWaiting / …Opened / …Closed / …Scheduled / …Cancelled`) are enforced end-to-end: `ClientStrategy.tick()/backtest()`, `StrategyCoreService`, the persistence layer, and every notification contract (`SignalOpenedNotification`, `SignalClosedNotification`, `SignalCancelledNotification`, `SignalScheduledNotification`) carry the lifecycle state explicitly. `validation.test.mjs` exercises valid long/short, inverted TP/SL, negative prices, and future timestamps; `backtest.test.mjs` walks every close reason (`take_profit`, `stop_loss`, `time_expired`).

</details>

### 5. The order the exchange silently rejected

Live trading's quiet killer: the exchange rejects, times out, or fills partially, and your bot's internal state no longer matches reality. The textbook "fix" is hand-written `try/catch` rollback around every order — which is exactly the code that breaks on the edge case you didn't think of.

Here, every state-mutating action fires through the broker adapter *before* the internal state changes. If the adapter throws — rejection, timeout, network failure — the mutation is skipped, the state stays exactly as it was, and the engine retries on the next tick. You never write rollback logic, and there is no half-applied state to reconcile. In backtest mode no adapter is called at all, so historical replays never touch exchange code.

<details>
<summary>The Code</summary>

The reusable core: place → poll to fill → on timeout cancel, market-out any partial fill, restore TP/SL so the position is never left naked, then throw so the engine retries.

```typescript
async function createLimitOrderAndWait(exchange, symbol, side, qty, price, restore?) {
  const order = await exchange.createOrder(symbol, 'limit', side, qty, price);

  for (let i = 0; i < FILL_POLL_ATTEMPTS; i++) {
    await sleep(FILL_POLL_INTERVAL_MS);
    if ((await exchange.fetchOrder(order.id, symbol)).status === 'closed') return; // filled
  }

  await exchange.cancelOrder(order.id, symbol);
  await sleep(CANCEL_SETTLE_MS);                       // let the exchange settle before reading

  const filledQty = (await exchange.fetchOrder(order.id, symbol)).filled ?? 0;
  if (filledQty > 0) {                                 // roll the partial fill back to clean state
    await exchange.createOrder(symbol, 'market', side === 'buy' ? 'sell' : 'buy', filledQty);
  }
  if (restore) { /* re-place TP + stop-loss on the remaining position so it is never unprotected */ }

  throw new Error('not filled in time — partial fill rolled back, backtest-kit will retry');
}
```

A hook wires it to position open. Signal open/close are routed automatically by an internal event bus the moment `Broker.enable()` is called — no manual wiring. The other mutations are intercepted explicitly before their state change:

```typescript
Broker.useBrokerAdapter(class implements IBroker {
  async waitForInit() { await getExchange(); }

  async onOrderOpenCommit({ symbol, cost, priceOpen, priceTakeProfit, priceStopLoss }) {
    const ex = await getExchange();
    const qty = truncateQty(ex, symbol, cost / priceOpen);
    await createLimitOrderAndWait(ex, symbol, 'buy', qty, priceOpen);   // entry
    try {                                                                // protect immediately
      await ex.createOrder(symbol, 'limit', 'sell', qty, priceTakeProfit);
      await createStopLossOrder(ex, symbol, qty, priceStopLoss);
    } catch (err) { await ex.createOrder(symbol, 'market', 'sell', qty); throw err; }
  }
  // onOrderCloseCommit · onPartialProfitCommit · onPartialLossCommit
  // onTrailingStopCommit · onTrailingTakeCommit · onBreakevenCommit · onAverageBuyCommit
});
Broker.enable();
```

Complete, production-grade **Spot** (`stop_loss_limit`, balance truncation, dust/notional guards) and **Futures** (`reduceOnly`, hedge-mode `positionSide`, `setLeverage`, ghost-position guards) adapters — every hook, every edge case — ship verbatim in the docs. The CLI can also dry-fire any single hook against your live adapter for verification before you wait hours for a real signal:

```bash
npx @backtest-kit/cli --brokerdebug --commit signal-open --symbol BTCUSDT
```

</details>

### 6. Averaging up is how a dip becomes a margin call

Dollar-cost averaging is where hand-rolled position math quietly bankrupts people. Average into a *rising* price by accident and you've raised your cost basis on a losing-direction trade — the opposite of the intent. And once you add partial closes on top, the cost-basis bookkeeping becomes a second strategy you have to get right.

`commitAverageBuy` is, by default, *only* accepted when price is below the running effective entry — averaging up is silently rejected, structurally. The effective price is a cost-weighted harmonic mean (correct for fixed-dollar entries, where $100 buys different quantities at different prices), and every partial close snapshots its cost basis so PnL replays exactly without re-walking history. No math required from you — the guardrail is in the engine.

<details>
<summary>The Math</summary>

```
effectivePrice = Σcost / Σ(cost / price)          // cost-weighted harmonic mean
```

Each partial stores `costBasisAtClose` (the running dollar basis *before* it fired); a partial sell does not change the effective price of the coins still held. Final PnL is a dollar-weighted sum across every partial (each at its own effective price) plus the remainder, with slippage and per-leg fees:

```
weight[i]        = (percent[i]/100 × costBasisAtClose[i]) / totalInvested
totalWeightedPnl = Σ weight[i]·pnl[i] + remainingWeight·pnlRemaining
pnlPercentage    = totalWeightedPnl − fees       // open fee once + per-partial + final close
pnlCost          = pnlPercentage / 100 × totalInvested
```

Worked example — LONG @1000, 4 accepted DCA + 1 rejected, 3 partials, close @1200 — reconciles two independent ways to **+17.9%**:

```
0.075·(+15.00) + 0.135·(−7.98) + 0.316·(+12.91) + 0.474·(+29.04) ≈ +17.89%
coin cross-check:  (34.50 + 49.69 + 142.72 + 244.67 − 400) / 400 ≈ +17.90% ✓
entry #5 @980 REJECTED — 980 > effective entry ≈929.92  (the guard firing)
```

</details>

<details>
<summary>The Code</summary>

A complete DCA-ladder strategy — open once, average on overlap-free dips up to 10 rungs, close at target — is about thirty lines, and the dangerous math is all inside the engine:

```typescript
import { addStrategySchema, listenActivePing, Position,
         commitAverageBuy, commitClosePending,
         getPositionEntries, getPositionEntryOverlap, getPositionPnlPercent } from 'backtest-kit';

addStrategySchema({
  strategyName: 'apr_2026_strategy',
  getSignal: async (symbol, when, currentPrice) => ({
    position: 'long',
    ...Position.moonbag({ position: 'long', currentPrice, percentStopLoss: 25 }),
    minuteEstimatedTime: Infinity, cost: 100,
  }),
});

listenActivePing(async ({ symbol, currentPrice }) => {                       // the ladder
  if ((await getPositionEntries(symbol)).length >= 10) return;
  if (await getPositionEntryOverlap(symbol, currentPrice, { upperPercent: 5, lowerPercent: 1 })) return;
  await commitAverageBuy(symbol, 100);                                        // rejected if it averages up
});

listenActivePing(async ({ symbol }) => {                                     // exit on blended target
  if (await getPositionPnlPercent(symbol) < 3) return;
  await commitClosePending(symbol, { id: 'unknown', note: '# closed by target pnl' });
});
```

Every order primitive is here, each with per-entry PnL, peak-profit and max-drawdown tracking: market/limit entries, TP/SL/OCO exits, grid with auto-cancel, partial profit/loss levels, trailing take/stop (absorbed only when they tighten in your favour, computed from the *original* distance to avoid drift), breakeven (moves the stop to entry once profit clears fees+slippage), stop-limit entries, DCA, and time-attack / infinite-hold.

</details>

### 7. Ten strategies, one account, 100% exposure

Per-strategy risk checks miss the obvious portfolio truth: ten strategies each "risking 10%" is one account risking everything. Risk validation here runs across *all* strategies and symbols at once, with an atomic check-and-reserve that closes the race between "is this allowed?" and "the order went out."

<details>
<summary>The Code</summary>

```typescript
addRiskSchema({
  riskName: 'demo',
  validations: [
    ({ pendingSignal, currentPrice }) => {                              // TP ≥ 1%
      const { priceOpen = currentPrice, priceTakeProfit, position } = pendingSignal;
      const tp = position === 'long'
        ? ((priceTakeProfit - priceOpen) / priceOpen) * 100
        : ((priceOpen - priceTakeProfit) / priceOpen) * 100;
      if (tp < 1) throw new Error(`TP too close: ${tp.toFixed(2)}%`);
    },
    ({ pendingSignal, currentPrice }) => {                              // R/R ≥ 2:1
      const { priceOpen = currentPrice, priceTakeProfit, priceStopLoss, position } = pendingSignal;
      const reward = position === 'long' ? priceTakeProfit - priceOpen : priceOpen - priceTakeProfit;
      const risk   = position === 'long' ? priceOpen - priceStopLoss : priceStopLoss - priceOpen;
      if (reward / risk < 2) throw new Error('Poor R/R ratio');
    },
  ],
});

listenRisk(async (event) => { await Risk.dump(event.symbol, event.strategyName); }); // every rejection, logged
```

`ClientRisk` tracks every open position across the portfolio; multiple strategies can share one profile for holistic exposure. `checkSignalAndReserve` is the thread-safe variant — after a successful reserve you **must** `addSignal` (finalize) or `removeSignal` (cancel) so reservations never go stale. A real LLM-gated portfolio improved from **+52.22% → +68.90%** PNL, Sharpe **+0.309 → +0.512**, win-rate **68% → 82%** simply by letting a local model veto 6 signals — 4 of them losers.

</details>

### 8. One process can trade the whole market

Spawning a process per symbol burns CPU on IPC and turns shared state — global risk, candle cache — into a distributed-systems problem you didn't sign up for. Dozens of symbols run concurrently here inside a **single Node process**, sharing one event loop, one Mongo pool, one Redis cache, with strict per-symbol state isolation.

<details>
<summary>The Proof</summary>

Measured on a commodity laptop (HP Victus, i5-13420H, 16 GB DDR4, NVMe SSD), 9 symbols in parallel, one Node process:

| Metric | Value |
|---|---|
| Wall-clock span (first → last event) | **2,893 ms** |
| Events captured | **297** |
| Historical time advanced / symbol | **34 minutes** |
| Per-symbol replay speed | **≈703×** real-time |
| Aggregate (9 symbols) | **≈6,326×** real-time |
| Hot-loop throughput | **≈103 events/sec** |

Why it's fast: single-process concurrency (no IPC, no fork), an in-memory activity registry (`Lookup`) tracking every in-flight workload, a cooperative event-loop hand-off (`Candle.spinLock`) so parallel symbols advance round-robin instead of one hogging the CPU, Redis O(1) candle lookups, atomic `findOneAndUpdate` upserts (no read-modify-write), and `--cache` pre-warming so the inner loop never blocks on HTTP.

In live mode the bottleneck moves from CPU to the exchange — and that is where the shared cache earns its keep. Every symbol pulls candles, order books, and trades through one **deduplicated** layer, so nine strategies asking for the same `BTCUSDT 1m` candle issue *one* request, not nine. Hand-written per-bot code with no cache hammers the REST endpoint until the exchange rate-limits it; here the dedup + Redis O(1) layer keeps request volume flat as you add symbols, so rate limits stay off your back instead of throttling the desk. The ×700 / ×6,300 figures are CPU-bound backtest replay; live throughput is paced by the exchange, but the request layer is built so that pacing is the exchange's published limit, not self-inflicted spam.

```typescript
import { Backtest, warmCandles } from 'backtest-kit';

for (const symbol of ['BTCUSDT','ETHUSDT','SOLUSDT','BNBUSDT','XRPUSDT']) {
  await warmCandles({ exchangeName: 'binance', interval: '1m', symbol,
    from: new Date('2026-02-01T00:00:00Z'), to: new Date('2026-02-28T23:59:59Z') });
  Backtest.background(symbol, { strategyName, exchangeName: 'binance', frameName: 'feb-2026' });
}
```

```bash
npx @backtest-kit/cli --backtest --entry ./content/multi-symbol.ts   # CLI defers symbol selection to your file
```

</details>

### 9. When `./dump/` stops being enough

File storage is perfect on day one and a bottleneck the day you're doing thousands of context-keyed reads per second. Swap to MongoDB (durable, queryable, atomic) with a Redis O(1) cache via a single `setup()` — all 15 persistence contracts reimplemented, and **not one line of strategy code changes.**

<details>
<summary>The Code</summary>

```typescript
// config/setup.config.ts — loaded once before any persistence call
import { setup } from '@backtest-kit/mongo';
setup();   // reads CC_MONGO_CONNECTION_STRING / CC_REDIS_* from env, or pass explicitly
```

Fifteen adapters, each with a unique compound index (`Signal → symbol+strategyName+exchangeName`, `Candle → symbol+interval+timestamp`, `Memory → signalId+bucketName+memoryId`, …). Candle records are immutable (`$setOnInsert`, first write wins); Measure/Interval/Memory use soft delete (`removed` flag) for an audit trail. Reads go Redis-first for the Mongo `_id`, then `findById` — two O(1) ops; a miss falls back to an indexed `findOne` and backfills. Writes are one `findOneAndUpdate({ upsert:true, new:true })` round-trip, so the unique index rejects concurrent duplicates at the storage engine and a write-then-read always sees fresh data. Signal-affecting adapters store the simulation `when`, so look-ahead protection is enforceable even inside the database.

```
read signal (BTCUSDT, my_strategy, binance)
  ├─ Redis GET → hit  → Mongo findById(_id)            ← O(1) + O(1)
  └─ Redis GET → miss → Mongo findOne(filter) → Redis SET → return
```

The default file adapter is already crash-safe (atomic temp+rename, repair on restart) — you get durability before you ever add a database.

</details>

### 10. A Sharpe of 10,000,000 is a bug, not an edge

Metrics that a tiny sample can't support are worse than no metrics — they're false confidence you bet money on. The analytics engine was rebuilt against canonical definitions and an independent 84-file reference testbed, and it prints **`N/A`** rather than a number it can't stand behind.

<details>
<summary>The Math</summary>

- **Pooled Sharpe** (v10.2.0+): per-trade returns are pooled across all symbols into one sample, then Sharpe is computed on that distribution — replacing the trade-count-weighted *average of ratios*, which inflates when one symbol is great and another negative. The header reads `Pooled Sharpe`, not `Portfolio Sharpe`, with a Markowitz disclaimer so it's never mistaken for covariance-based optimization.
- **Bessel's correction (N−1)** for unbiased variance — no risk underestimation on small samples.
- **Compounded equity curve** for Max Drawdown / Calmar / Recovery Factor — no double-counting of percentage returns.
- **Geometric annualization** for expected yearly returns — accounts for volatility drag (a 50% loss needs a 100% gain to recover).
- **Canonical Sortino (1991)** with downside deviation over `N_total`.
- **Float-artifact guard:** identical-return series produce stddev ≈1e-17; an `STDDEV_EPSILON` guard returns `N/A` instead of a fake Sharpe of 10,000,000. Gates of ≥10 signals and ≥14 calendar days gate publication.

Dashboard revenue is dollar-true: `pnlCost = pnlPercentage/100 × pnlEntries`, summed across closed signals per window (Today / Yesterday / 7d / 31d), anchored to the run end in backtest and `Date.now()` live.

</details>

### 11. The jobs that fire on virtual time

Most schedulers run on wall-clock — useless in a backtest that replays a month in three seconds. `Cron` runs on the *same* time stream your strategies see, firing on candle boundaries, coordinated across parallel backtests so one boundary never double-fires. The identical API drives live re-polling and one-shot backtest prep.

<details>
<summary>The Code</summary>

```typescript
import { Cron, Backtest } from 'backtest-kit';

Cron.register({ name: 'tg-parser', interval: '1h',                                 // global, hourly
  handler: async ({ when }) => { await parseTelegramSignals(when); } });

Cron.register({ name: 'funding', interval: '1h', symbols: ['BTCUSDT','ETHUSDT'],   // per-symbol fan-out
  handler: async ({ symbol, when }) => { await fetchFundingRate(symbol, when); } });

Cron.register({ name: 'warm-cache',                                                // fire-once, global
  handler: async () => { await warmupCache(); } });

Cron.enable();   // wire to engine lifecycle once; every tick is forwarded automatically
```

`enable()` merges four lifecycle subjects (`beforeStart`, `idlePing`, `activePing`, `schedulePing`) into one serial queue via `singlerun`; each tick is base-aligned to the minute. Coordination keys `${name}:${alignedMs}:${symbol?}:g${generation}` give mutex semantics — parallel backtests on the same boundary share one in-flight promise (first opens the slot, others await). Fire-once marks record only on success, so a failed handler retries; the generation suffix isolates re-registrations from late writes.

</details>

### 12. You shouldn't have to abandon TradingView or Python to use TypeScript

The honest objection to a TS trading engine is "but my indicators live in Pine Script and TA-Lib." So they don't have to move. Run native Pine Script, run Python via WASM, use 50+ built-in indicators, or drop in zero-dependency quant ports — all under the same temporal guarantees.

<details>
<summary>The Code</summary>

**Pine Script** — v5/v6, 60+ indicators, 1:1 syntax, look-ahead-safe ([`@backtest-kit/pinets`](https://www.npmjs.com/package/@backtest-kit/pinets)):

```typescript
import { File, getSignal } from '@backtest-kit/pinets';
const signal = await getSignal(File.fromPath('strategy.pine'),
  { symbol: 'BTCUSDT', timeframe: '5m', limit: 100 });   // plots: Signal/Close/StopLoss/TakeProfit/EstimatedTime
```

**50+ indicators across 1m/15m/30m/1h + order book, as LLM-ready Markdown, in one call** ([`@backtest-kit/signals`](https://www.npmjs.com/package/@backtest-kit/signals)):

```typescript
import { commitHistorySetup } from '@backtest-kit/signals';
await commitHistorySetup('BTCUSDT', messages);   // order book + candles + indicators, cached per TTL
```

**Typed DAG** of computations, resolved in topological order with `Promise.all` parallelism, serializable to a DB ([`@backtest-kit/graph`](https://www.npmjs.com/package/@backtest-kit/graph)):

```typescript
import { sourceNode, outputNode, resolve } from '@backtest-kit/graph';
const higher = sourceNode(async (symbol) => extract(await run(File.fromPath('timeframe_4h.pine'), { symbol, timeframe: '4h', limit: 100 }), { allowLong: 'AllowLong', allowShort: 'AllowShort', noTrades: 'NoTrades' }));
const lower  = sourceNode(async (symbol) => extract(await run(File.fromPath('timeframe_15m.pine'), { symbol, timeframe: '15m', limit: 100 }), { position: 'Signal', priceOpen: 'Close', priceTakeProfit: 'TakeProfit', priceStopLoss: 'StopLoss' }));
const mtf = outputNode(([h, l]) => {                          // combine; null when timeframes disagree
  if (h.noTrades || l.position === 0) return null;
  if (h.allowShort && l.position === 1) return null;
  if (h.allowLong  && l.position === -1) return null;
  return toSignalDto(randomString(), l, null);
}, higher, lower);
addStrategySchema({ strategyName: 'mtf', interval: '5m', getSignal: () => resolve(mtf) });
```

**Python via WASM (WASI)** runs `ta-lib`/`pandas`/`scikit-learn` indicators in the Node event loop with no IPC. And zero-dependency TS ports of the math behind vectorbt — see [See also](#-see-also).

</details>

### 13. AI strategies without ten provider SDKs

LLM-driven signals normally mean per-provider boilerplate and JSON you can't trust. One HOF API spans 10+ providers; structured output is schema-enforced; trading context is injected automatically.

<details>
<summary>The Code</summary>

```typescript
import { deepseek } from '@backtest-kit/ollama';
addStrategy({
  strategyName: 'llm-signal', interval: '5m',
  // swap deepseek() → claude() / gpt5() / ollama() with no other change
  getSignal: deepseek(getSignal, 'deepseek-chat', process.env.DEEPSEEK_API_KEY),
});
```

Providers: OpenAI, Claude, DeepSeek, Grok, Mistral, Perplexity, Cohere, Alibaba, Hugging Face, Ollama (local), GLM-4. Structured output is enforced with Zod / JSON schema via `addOutline` (auto-retry on malformed output, custom rules like "SL must be below entry for LONG"); token rotation accepts a key array; prompts live in `config/prompt/*.cjs` and are memoized to kill redundant backtest API calls. The full LLM strategy — fetch multi-timeframe candles, ask the model, dump the reasoning, return a validated signal:

```typescript
import { v4 as uuid } from 'uuid';
import { addStrategySchema, getCandles, dumpAgentAnswer, dumpRecord } from 'backtest-kit';
import { json } from './utils/json.mjs';
import { getMessages } from './utils/messages.mjs';

addStrategySchema({
  strategyName: 'llm-strategy', interval: '5m', riskName: 'demo',
  getSignal: async (symbol) => {
    const messages = await getMessages(symbol, {
      candles1h:  await getCandles(symbol, '1h', 24),
      candles15m: await getCandles(symbol, '15m', 48),
      candles5m:  await getCandles(symbol, '5m', 60),
      candles1m:  await getCandles(symbol, '1m', 60),
    });
    const resultId = uuid();
    const signal = await json(messages);                              // LLM → structured signal
    await dumpAgentAnswer({ dumpId: 'position-context', bucketName: 'mtf', messages, description: 'agent reasoning' });
    await dumpRecord({ dumpId: 'position-entry', bucketName: 'mtf', record: signal, description: 'signal params' });
    return { ...signal, id: resultId };
  },
});
```

Memory adapters persist LLM reasoning per signal (BM25 search, soft delete); `dumpAgentAnswer` archives the full conversation — roles, reasoning, tool calls — attached to the signal, so an opaque model decision becomes a debuggable record.

</details>

---

## The API assumes you will make every mistake

Read back through the rakes and a pattern shows: none of them are solved by *telling you to be careful*. Look-ahead bias isn't prevented by a lint rule — there's simply no timestamp to pass. Averaging up isn't discouraged in the docs — the call is rejected. A closed position's live PnL isn't a runtime guard — it doesn't compile. The whole surface is built on the assumption that you, or the model writing your strategy, will eventually do the wrong thing at 3 a.m. — so the wrong thing is made unreachable. This is the "pit of success": the easy path and the correct path are the same path.

And the shape of that surface is **reactive — React for traders.** You never write the time loop. You don't iterate candles, advance a clock, or poll for fills. You *declare reactions* to lifecycle events, and the engine owns the loop in both backtest and live. `getSignal` is your pure render function — given the current state of the world, return a signal or `null`. The `listen*` family is your effects layer — small handlers that fire when the position's state changes, exactly like subscribing to state in a component. Composition is additive: stack independent listeners and each one minds its own concern, the same way you'd split hooks.

<details>
<summary>The Code</summary>

`getSignal` declares *what* to open; the listeners declare *how the position behaves once alive* — a DCA ladder, a profit target, and an error sink, three independent reactions to the same event stream, no shared loop, no manual bookkeeping:

```typescript
import {
  addStrategySchema, listenActivePing, listenError, Log, Position,
  commitAverageBuy, commitClosePending,
  getPositionEntries, getPositionEntryOverlap, getPositionPnlPercent,
} from "backtest-kit";
import { errorData, getErrorMessage, str } from "functools-kit";

const HARD_STOP = 25, TARGET_PROFIT = 3, STEP = 100, MAX_STEPS = 10;

// render: given "now", declare the position to open (or null to stay flat)
addStrategySchema({
  strategyName: "apr_2026_strategy",
  getSignal: async (symbol, when, currentPrice) => ({
    position: "long",
    ...Position.moonbag({ position: "long", currentPrice, percentStopLoss: HARD_STOP }),
    minuteEstimatedTime: Infinity, cost: STEP,
  }),
});

// effect: average into dips, up to 10 overlap-free rungs (averaging up is rejected for you)
listenActivePing(async ({ symbol, currentPrice }) => {
  if ((await getPositionEntries(symbol)).length >= MAX_STEPS) return;
  if (await getPositionEntryOverlap(symbol, currentPrice, { upperPercent: 5, lowerPercent: 1 })) return;
  await commitAverageBuy(symbol, STEP);
});

// effect: close the whole position once blended PnL clears the target
listenActivePing(async ({ symbol, data }) => {
  if (await getPositionPnlPercent(symbol) < TARGET_PROFIT) return;
  Log.info("position closed due to the target pnl reached", { symbol, data });
  await commitClosePending(symbol, { id: "unknown", note: str.newline("# Closed by target pnl") });
});

// effect: a single place for anything that goes wrong
listenError((error) => Log.debug("error", { error: errorData(error), message: getErrorMessage(error) }));
```

The full reactive surface — subscribe to any point in a position's life and the engine fires it in order, queued, never overlapping: `listenSignal` / `listenSignalBacktest` / `listenSignalLive` (lifecycle), `listenActivePing` (per-minute while a position is live), `listenSchedulePing` / `listenIdlePing`, `listenPartialProfit` / `listenPartialLoss`, `listenBreakevenAvailable`, `listenHighestProfit`, `listenMaxDrawdown`, `listenRisk` (rejections), `listenError` / `listenExit`, `listenDone*`, plus `*Once` filtered variants for one-shot reactions. You compose behavior by adding handlers, not by editing a loop.

</details>

<details>
<summary>The Proof</summary>

The five guarantees that make the surface fool-proof, each enforced by the engine rather than by convention:

1. **Ambient temporal context** — no `currentDate`/`timestamp` parameter exists to forget; the engine resolves "now" from `AsyncLocalStorage` and blocks future data at the adapter level.
2. **Type-safe state machine** — `idle → scheduled → pending → opened → active → closed` as discriminated unions; calling a close on an already-closed signal, or editing an active trade's entry, is a compile error.
3. **Guarded DCA** — `commitAverageBuy` rejects any call that would worsen the harmonic-mean effective entry; you cannot accidentally average up.
4. **Transactional broker commits (the "no-try-catch" rule)** — the adapter intercepts every mutation before internal state changes; an exchange throw rolls back and retries on the next tick, so you never hand-write rollback.
5. **Automatic signal validation** — TP/SL soundness, R/R minimum, and interval throttling are checked before a signal reaches execution; invalid signals are logged or rejected, never run.

Because the loop belongs to the engine, the *same* declarations run identically in backtest and live — the reactive model is the reason "same code, both modes" is structurally true, not just aspirational.

</details>

---

## Receipts

Toy READMEs prove a moving-average crossover on daily candles. These are nine production-quality strategies, each a *different* signal source, each backtested on real history with the numbers written down. They live in [`/example`](https://github.com/tripolskypetr/backtest-kit/tree/master/example) — clone it, run it, get the same prints.

| Strategy | Ticker · Period | Signal source | Net PNL | Sharpe |
|---|---|---|---:|---:|
| [Neural Network](https://github.com/tripolskypetr/backtest-kit/tree/master/example/content/oct_2021.strategy) | BTC · Oct 2021 | TensorFlow NN (8→6→4→1) predicting next-candle close | **+18.26%** | 0.31 |
| [Python EMA Crossover](https://github.com/tripolskypetr/backtest-kit/tree/master/example/content/feb_2021.strategy) | DOT · Feb 2021 | EMA(9)/EMA(21) via WebAssembly (WASI) | **+5.52%** | 0.09 |
| [Polymarket Δprob](https://github.com/tripolskypetr/backtest-kit/tree/master/example/content/apr_2024.strategy) | BTC · Apr 2024 | Prediction-market probability shifts | **+0.63%** | 0.065 |
| [Pine Script Range Breakout](https://github.com/tripolskypetr/backtest-kit/tree/master/example/content/dec_2025.strategy) | BTC · Dec 2025 | Bollinger + range + volume spike (Pine) | **+2.40%** | 0.06 |
| [Liquidity Harvesting](https://github.com/tripolskypetr/backtest-kit/tree/master/example/content/jan_2026.strategy) | TRX · Jan 2026 | Telegram channel signals, **inverted** | **+8.58%** | **1.14** |
| [AI News Sentiment](https://github.com/tripolskypetr/backtest-kit/tree/master/example/content/feb_2026.strategy) | BTC · Feb 2026 | LLM on live news (Tavily + Ollama) | **+16.99%** | 0.25 |
| [SHORT DCA Ladder](https://github.com/tripolskypetr/backtest-kit/tree/master/example/content/mar_2026.strategy) | BTC · Mar 2026 | Fixed SHORT + ladder up (≤10 rungs) | **+37.83%** | 0.35 |
| [LONG DCA Ladder](https://github.com/tripolskypetr/backtest-kit/tree/master/example/content/apr_2026.strategy) | BTC · Apr 2026 | Fixed LONG + ladder down (≤10 rungs) | **+67.85%** | 0.12 |
| [Crowd Liquidity](https://github.com/tripolskypetr/backtest-kit/tree/master/example/content/jun_2026.strategy) | BTC · Jun 2026 | TradingView ideas, `Simulator`-trained author whitelist | **+19.80%** | **0.64** |

<details>
<summary>The Proof</summary>

- **Liquidity Harvesting (Sharpe 1.14)** — a Telegram channel published SHORT signals with ~0.375:1 R/R and 106% deposit at risk at 25× leverage, mathematically guaranteed to lose; a volume spike appeared 15 min before every post and the TP step multipliers were identical across signals — an algorithm. Inverting it turned **−5.05% → +8.58%**, profit factor **0.56 → 7.31**. The edge was the bot crowd, not the indicators.
- **Crowd Liquidity** — 462 TradingView ideas, 167 authors, and a falling knife of a month (BTC −20.4%). Following the raw crowd lost −24.23%; the framework's `Simulator` entity grid-searched the author ban rule itself (minimum track × minimum hit rate among 3,456 points) and kept **5 authors of 167**. Trading any post of those five: 10 trades, 90% WR, **+19.80%** — the whitelist artifact is generated by `scripts/simulator.mjs`, not curated by hand. Stated caveat: trained and traded on the same month; the July out-of-sample is the open question.
- **AI News Sentiment** held SHORT through nearly all of a −16.4% month, flipped to LONG on the recovery bounce, and flipped back on geopolitical news — **+16.99%** where buy-and-hold lost 16%.
- **DCA Ladders** show the trade-off honestly: high % return on deployed capital, but absolute fiat risk grows with rungs (Mar: −$104.93 on a 10-rung position; theoretical max −$2,500 if a non-reverting trend hits the 25% hard stop with all rungs filled). The README states the downside, not just the upside.

Every example documents price context, trade log, equity curve, and risk analysis — and several ship a `--noDCA` / single-entry variant so you can see exactly what the position management bought you.

</details>

---

## How it sits next to the alternatives

The honest version: for a quick research prototype or a single MA crossover, VectorBT or Backtrader are hard to beat on raw speed. The moment you need to *deploy* — complex position sizing, AI agents, a network outage that mustn't desync your bot — is where the guardrails below start to matter.

| | Backtest Kit | Backtrader | VectorBT | MetaTrader/MQL5 | QuantConnect | Freqtrade |
|---|---|---|---|---|---|---|
| Language | TypeScript | Python | Python | MQL5 | C#/Python | Python |
| Live trading | ✅ built-in | ⚠️ manual | ❌ research | ✅ | ✅ | ✅ |
| Look-ahead prevention | ✅ engine-enforced | ⚠️ discipline | ⚠️ discipline | ⚠️ discipline | ⚠️ partial | ⚠️ partial |
| Crash-safe persistence | ✅ atomic + Mongo | ❌ | ❌ | ❌ | ⚠️ cloud | ⚠️ basic |
| Transactional broker | ✅ auto rollback | ❌ | ❌ | ❌ | ⚠️ partial | ⚠️ basic |
| Type-safe state machine | ✅ compile-time | ❌ | ❌ | ❌ | ❌ | ❌ |
| DCA / partial closes | ✅ first-class | ⚠️ manual | ⚠️ manual | ⚠️ manual | ⚠️ manual | ⚠️ limited |
| AI / LLM integration | ✅ built-in | ❌ | ❌ | ❌ | ⚠️ custom | ❌ |
| Pine Script | ✅ native | ❌ | ❌ | ✅ | ❌ | ❌ |
| Self-hosted | ✅ 100% | ✅ | ✅ | ⚠️ desktop | ❌ cloud | ✅ |

Open-source QuantConnect/MetaTrader without the lock-in: pure TypeScript, your code, your data, your machines, no platform fees, no proprietary GUI. Drop any library into `getSignal` — Ollama, [`neural-trader`](https://www.npmjs.com/package/neural-trader), your own.

---

## 🌍 Ecosystem

**The core is a library; the CLI is the framework on top — and the framework is optional.** Think React vs Next.js. `backtest-kit` (the reactive engine — `getSignal` + the `listen*`/`commit*` API) is the library you build against directly. `@backtest-kit/cli` is the Next.js: it wires the runner, candle cache, dashboard, Telegram, and graceful shutdown so you don't have to — but you can ignore it entirely and call `Backtest.run()` / `Live.background()` yourself. `@backtest-kit/sidekick` is the explicit middle ground — it scaffolds a project where every wire (exchange adapter, frames, risk rules, strategy, runner) lives as plain, editable source in **your** userspace, with no CLI in the loop and nothing hidden. You pick how much magic you want.

On the "dependency zoo": every package below is authored by one team and shipped by the commercial vendor [TheOneTrade](https://theonetrade.github.io) — versioned together, released together. Treat it like the .NET base class library: a single coherent contract where the userspace surface (`getSignal`, `commit*`, `listen*`, `get*`) does not churn under you between releases. You install only what a given strategy needs, and the heavy or platform-specific pieces (Python-via-WASM, TensorFlow builds) sit behind their own optional packages so the core install stays clean and conflict-free.

### `@backtest-kit/cli` — [npm](https://www.npmjs.com/package/@backtest-kit/cli)
Zero-boilerplate runner. Modes: `--backtest / --paper / --live / --walker / --main / --pine / --editor / --dump / --pnldebug / --brokerdebug / --flush / --init / --docker`. Auto candle caching, monorepo cwd-resolution with per-strategy `.env` override, folder-based import aliases, broker module hooks, `setup.config` / `loader.config` / `alias.config`, graceful SIGINT.
```bash
npx -y @backtest-kit/cli --init
```

### `@backtest-kit/pinets` — [npm](https://www.npmjs.com/package/@backtest-kit/pinets)
Run TradingView Pine Script v5/v6 in Node, 60+ indicators, 1:1 syntax, `getSignal` / `run` / `extract` / `extractRows`.
```bash
npm install @backtest-kit/pinets pinets backtest-kit
```

### `@backtest-kit/graph` — [npm](https://www.npmjs.com/package/@backtest-kit/graph)
Compose computations as a typed DAG; resolved in topological order with `Promise.all`, serializable to a DB for storage.
```bash
npm install @backtest-kit/graph backtest-kit
```

### `@backtest-kit/ui` — [npm](https://www.npmjs.com/package/@backtest-kit/ui)
React/MUI dashboard with Lightweight Charts: live signal-lifecycle state-machine view, per-signal inspection, risk/partial/trailing/breakeven views, manual control, Pine editor.
```typescript
import { serve } from '@backtest-kit/ui';
serve('0.0.0.0', 60050);   // http://localhost:60050
```

### `@backtest-kit/mongo` — [npm](https://www.npmjs.com/package/@backtest-kit/mongo)
MongoDB source-of-truth + Redis O(1) cache. All 15 persistence contracts, atomic upserts, soft delete, look-ahead-safe `when`. Zero strategy changes.
```bash
npm install @backtest-kit/mongo backtest-kit mongoose ioredis
```

### `@backtest-kit/pg` — [npm](https://www.npmjs.com/package/@backtest-kit/pg)
PostgreSQL + Redis O(1) cache via TypeORM. All 15 persistence contracts, atomic upserts, soft delete, look-ahead-safe `when`. Tuned for Pgpool-II so read fan-out scales across replicas: up to ~4× faster
```bash
npm install @backtest-kit/pg backtest-kit typeorm pg ioredis reflect-metadata
```

### `@backtest-kit/minio` — [npm](https://www.npmjs.com/package/@backtest-kit/minio)
MinIO (S3) source-of-truth + Redis time-ordered index. Listings in O(limit), zero schema management. Zero strategy changes.
```bash
npm install @backtest-kit/minio backtest-kit minio ioredis
```

### `@backtest-kit/ollama` — [npm](https://www.npmjs.com/package/@backtest-kit/ollama)
Universal LLM adapter: 10+ providers, structured output, token rotation, fallback chains, trading-context injection.
```bash
npm install @backtest-kit/ollama agent-swarm-kit backtest-kit
```

### `@backtest-kit/signals` — [npm](https://www.npmjs.com/package/@backtest-kit/signals)
50+ indicators across 4 timeframes + order book, multi-timeframe synchronized, LLM-ready Markdown reports.
```bash
npm install @backtest-kit/signals backtest-kit
```

### `@backtest-kit/sidekick` — [npm](https://www.npmjs.com/package/@backtest-kit/sidekick)
The "eject" of `--init`: scaffolds a project where exchange adapter, frames, risk rules, strategy, and runner are all editable source. 4H-trend + 15m-signal Pine template, partial profit taking, breakeven trailing.
```bash
npx -y @backtest-kit/sidekick my-trading-bot && cd my-trading-bot && npm start
```

---

## 👨‍👩‍👦 Community

Real, runnable templates — not slideware. And worth naming the concern directly: yes, this is one author's ecosystem, which is exactly what makes it *coherent* — but coherent is not captive. Everything is **MIT and open-source**, the core engine has **zero hard dependency** on any `@backtest-kit/*` add-on (you can run `getSignal` + `listen*` against a bare `addExchangeSchema` and nothing else), and each repo below is an independent reference you're meant to **fork and own**. The lock-in you'd normally fear — a closed runtime, a proprietary data format, a cloud you can't leave — none of it applies; the persistence is plain files or your own Mongo, the signals are your code, and the exit cost is a `git clone`.

- **[backtest-monorepo-parallel](https://github.com/backtest-kit/backtest-monorepo-parallel)** — 9 symbols in parallel in one Node process on shared Mongo+Redis, ~6,300× real-time, self-enforcement runtime exposing the workspace DI container to `./content/` strategy files. The scaling recipe: +1 service = +1 file, +1 provider, +1 ioc entry.
- **[backtest-ollama-crontab](https://github.com/backtest-kit/backtest-ollama-crontab)** — a local Ollama (`gpt-oss` quantized) as a per-signal risk gate plus a 15-minute crontab ingesting any public Telegram channel; the *same code* re-polls live and bulk-prepares in backtest. Documented result: **+52.22% → +68.90%** with the LLM gate on.
- **[backtest-kit-redis-mongo-docker](https://github.com/backtest-kit/backtest-kit-redis-mongo-docker)** — production persistence: all 15 adapters on Mongo+Redis, atomic read-after-write, `docker-compose` one-command deploy.
- **[backtest-kit-redis-postgres-pgpool-docker](https://github.com/backtest-kit/backtest-kit-redis-postgres-pgpool-docker)** — backtest-kit persistence on PostgreSQL (Pgpool-II) + Redis cache, with atomic upserts and a replica cluster.
- **[backtest-kit-minio-s3-docker](https://github.com/backtest-kit/backtest-kit-minio-s3-docker)** — persistence on MinIO (S3) with deterministic keys, S3-grade durability
- **[backtest-kit-skills](https://github.com/backtest-kit/backtest-kit-skills)** — a Claude Code skill + Mintlify docs: describe a strategy in plain language, get working TypeScript with every schema registration wired. `npx skills add https://github.com/backtest-kit/backtest-kit-skills`
- **[uzse-backtest-app](https://github.com/backtest-kit/uzse-backtest-app)** — Pine Script on regional exchanges that aren't on TradingView (UZSE, MSE, DSE…): download raw trades, build candles, feed them through a custom Mongo exchange adapter.
- **[backtest-kit-docs](https://github.com/backtest-kit/backtest-kit-docs)** — Architecture handbook and knowledge base: explains the engine's design, AI workflows, production patterns, and quantitative trading concepts beyond the API.

---

## 🪐 See also

Zero-dependency TypeScript ports of the quant math behind [vectorbt](https://github.com/polakowo/vectorbt) — same models, native to the `Exchange` schema, no Python runtime. Each estimates a different dimension of speculative pressure and plugs in independently:

- **[garch](https://www.npmjs.com/package/garch)** — conditional variance of log-returns (GARCH / EGARCH / GJR-GARCH / HAR-RV / NoVaS, auto-selected by QLIKE) to bound how far flow can push price next candle; fitted `σ` → log-normal corridor `P·exp(±z·σ)` for TP/SL. Via `Exchange.getCandles`.
- **[pump-anomaly](https://www.npmjs.com/package/pump-anomaly)** — coordinated-speculation detection: cross-correlation + union-find author clustering separates real multi-actor inflow from one actor on many channels; volume z-scores score cascade pressure (pump vs stop-hunt). Returns an entry/exit plan, exits fitted by OHLC replay and screened against winner's-curse (DSR / PBO / SPA). Via `Exchange.getRawCandles`.
- **[volume-anomaly](https://www.npmjs.com/package/volume-anomaly)** — order-flow intensity: Hawkes branching ratio (arrival clustering), CUSUM (imbalance shift), BOCPD (regime break) → composite outlier score as an entry-timing gate. Via `Exchange.getAggregatedTrades`.

---

## 🌐 Internationalization

The `@backtest-kit/ui` dashboard ships in **7 languages**: English, Русский, Türkçe, 中文, हिन्दी, Español, Português. Switch via the language picker in the header

<details>
<summary>Locales</summary>


- 🇬🇧 **English** — ~1.5B speakers. Backtest Kit is a TypeScript engine where the strategy you test on history is byte-for-byte the one that trades live — only the clock changes. It removes the failure modes that kill bots (look-ahead bias, crash corruption, silent order rejects, averaging up) at the API level, then adds first-class DCA, partial closes, portfolio risk, and AI/Pine signals on top.

- 🇨🇳 **中文** — ~1.1B speakers. Backtest Kit 是一个 TypeScript 引擎：在历史数据上回测的策略代码，与实盘运行的代码逐字节一致，唯一区别只是时钟来源。它在 API 层面消除了让交易机器人崩溃的隐患（未来函数、崩溃损坏、静默拒单、越买越亏），并内置分批建仓、部分平仓、组合风控以及 AI/Pine 信号。

- 🇮🇳 **हिन्दी** — ~600M speakers. Backtest Kit एक TypeScript इंजन है जिसमें इतिहास पर परखा गया कोड ही बिना बदलाव के लाइव ट्रेड करता है — केवल घड़ी बदलती है। यह बॉट को बर्बाद करने वाली गलतियाँ (लुक-अहेड बायस, क्रैश करप्शन, चुपचाप ऑर्डर रिजेक्ट, ऊपर औसत करना) API स्तर पर ही रोकता है, और ऊपर से DCA, आंशिक क्लोज़, पोर्टफोलियो जोखिम व AI/Pine सिग्नल देता है।

- 🇪🇸 **Español** — ~560M speakers. Backtest Kit es un motor TypeScript donde la estrategia que pruebas con datos históricos es, byte a byte, la que opera en vivo — solo cambia el reloj. Elimina en la propia API los fallos que arruinan bots (sesgo look-ahead, corrupción por caídas, rechazos silenciosos de órdenes, promediar al alza) y suma DCA, cierres parciales, riesgo de cartera y señales de IA/Pine.

- 🇧🇷 **Português** — ~260M speakers. Backtest Kit é um motor TypeScript em que a estratégia testada no histórico é, byte a byte, a mesma que opera ao vivo — só o relógio muda. Ele elimina no próprio API os erros que matam bots (viés look-ahead, corrupção por falha, rejeição silenciosa de ordens, preço médio para cima) e ainda oferece DCA, fechamentos parciais, risco de carteira e sinais de IA/Pine.

- 🇷🇺 **Русский** — ~255M speakers. Backtest Kit — TypeScript-движок, где стратегия, проверенная на истории, побайтово совпадает с той, что торгует вживую: меняются только часы. Он устраняет на уровне API ошибки, губящие ботов (заглядывание в будущее, порча состояния при сбое, тихий отказ ордера, усреднение вверх), и добавляет полноценный DCA, частичные закрытия, портфельный риск и сигналы от AI/Pine.

- 🇹🇷 **Türkçe** — ~90M speakers. Backtest Kit, geçmiş veride test ettiğiniz stratejinin canlıda bayt bayt aynısını çalıştıran bir TypeScript motorudur — yalnızca saat değişir. Botları çökerten hataları (look-ahead yanlılığı, çökme bozulması, sessiz emir reddi, yukarı ortalama) API düzeyinde ortadan kaldırır; üstüne DCA, kısmi kapanışlar, portföy riski ve AI/Pine sinyalleri ekler.

</details>

---

## 🔒 Connection-loss protection

A dropped connection must never cost you a position — or buy one twice. Every broker conversation (order open, order close, liveness check) carries a bounded retry counter delivered to your adapter as `payload.attempt`: rejected opens retry with the **same signalId**

<details>
<summary>The three typed errors</summary>

Tag exchange orders with `clientOrderId = signalId`, and at `attempt > 0` query the prior order by that id *before* re-sending — if it filled, confirm the open instead of buying again (don't rely on catching "duplicate": Binance's guard only covers open orders, an instantly-filled one won't dup). The counter is pre-armed into persistence before each attempt, so `attempt > 0` holds even across a crash mid-attempt (see `CC_ORDER_OPEN_RETRY_ATTEMPTS`); rejected closes retry up to `CC_ORDER_CLOSE_RETRY_ATTEMPTS` and then force-close the engine state with the original reason; failed checks are tolerated for `CC_ORDER_CHECK_RETRY_ATTEMPTS` consecutive ticks instead of killing a live position on the first blip. When the network truly won't let the engine work, it exits loudly (`listenExit`) rather than churning forever. Your adapter states intent by throwing one of three typed errors — anything untyped is treated as transient.

- 🌩️ **`OrderTransientError`** — "temporary failure, retry me" (timeout, 5xx, rate limit, lost response). Pure declarative sugar: any plain `Error` behaves identically, the framework never pattern-matches this class. Opens retry identity-stably with the same `signalId`, closes retry next tick, checks keep the order alive — each bounded by its `CC_ORDER_*_RETRY_ATTEMPTS` config (default 5, `0` = legacy behavior). Exhausting the budget is the one path that signals a fatal exit. `OrderTransientError.fromError(e)` wraps a caught error while keeping its message.

- ⛔ **`OrderRejectedError`** — terminal business refusal, for the **gates** (`onOrderOpenCommit` / `onOrderCloseCommit`): "the exchange definitively said no — retrying is pointless" (min-notional, delisted symbol, no counterparty). An open is dropped at once without arming the retry; a close force-closes the engine state immediately with the original reason — the close lifecycle event still reaches the adapter so the real position can be reconciled. No fatal exit: a business outcome keeps the process alive. Thrown from a check instead, it is a protocol violation and intentionally degrades to transient.

- 🗑️ **`OrderDeletedError`** — confirmed order-not-found, for the **checks** (`onOrderActiveCheck` / `onOrderScheduleCheck`): "the exchange reports no order under this id" (cancelled manually, liquidated externally). Acts terminally at once, bypassing the tolerance counter: an open position closes with reason `closed`, a resting order cancels with reason `user`. A **filled** order is not a deleted order — confirm fills via `commitActivateScheduled` / `commitCreateTakeProfit` / `commitCreateStopLoss` so the close carries its true reason. Thrown from a gate instead, it degrades to transient.

</details>

---

## 💯 Tested

1030+ unit and integration tests cover exchange helpers, the event-listener system, signal validation (valid long/short, inverted TP/SL, negative prices, future timestamps), PnL accuracy with 0.1% fees + 0.1% slippage, the full lifecycle and every close reason, strategy callbacks, and report generation. Tests use unique schema names per case (no cross-contamination), a forward-progressing mock candle generator, and event-driven completion detection.

<details>
<summary>Core test axes</summary>

 - ✅ State machine under rejections (gates, throttle rollbacks, terminal drops, stopStrategy race)
 - ✅ Deferred commands, Live × Backtest
 - ✅ Broker: 8-stage lifecycle routing, gates, backtest silence, enable/disable, commit\* layer
 - ✅ Position commands + interleaved DCA × partial exits
 - ✅ Context-free surface (62 bare calls)
 - ✅ Crash recovery of every deferred flag + commit queue
 - ✅ SHORT mirror of the key paths 
 - ✅ Timeouts, Once-listeners, action gate, Infinity holds, whipsaw restore, shared-risk contention, cancellation stats
 - ✅ Order events: types, emission/silence per mode
 - ✅ Look-ahead bias protection: candle alignment, pending-candle exclusion, `getNextCandles` throwing in live
 - ✅ Signal validation: inverted TP/SL, negative/NaN/Infinity prices, future timestamps, micro-profit eaten by fees, excessive lifetime
 - ✅ Full lifecycle and every close reason: take_profit, stop_loss, time_expired, user close, external "closed"
 - ✅ Scheduled signals: price/wick activation, pre-activation SL cancellation, timeout, frame-end, immediate activation
 - ✅ Money-safety edges: SL-before-activation, TP-vs-SL priority on a single candle, extreme volatility, exchange errors surfaced not swallowed
 - ✅ Signal queues: sequences of mixed outcomes, winning/losing streaks, deterministic-id retry vs whipsaw block
 - ✅ Infinity holds: chunked candle processing across frame boundaries, close-at-boundary edge cases
 - ✅ PnL math: 0.1% fees + 0.1% slippage, cost-basis snapshots, partial-close weighting, DCA harmonic effective price
 - ✅ Partial profit/loss: dollar-exact closes on the remaining cost basis, 100%-of-remaining epsilon cap
 - ✅ Trailing stop/take: percentage-point shifts from ORIGINAL levels, absorption, intrusion protection
 - ✅ Breakeven: threshold math over fees+slippage, trailing upgrade paths, zero-risk exits landing exactly on entry
 - ✅ Risk schemas: reservation/release accounting, rejection callbacks, shared risk maps across strategies
 - ✅ Position sizing: fixed/percent/Kelly calculators, min/max caps ordering
 - ✅ Persistence: atomic writes, JSON adapters, restore after restart (pending, scheduled, deferred flags, commit queue), context-mismatch skips, Infinity round-trip
 - ✅ Candle cache: hit/miss keys, interval separation, adapter call counting
 - ✅ Event-listener system: every `listen*`/`listen*Once` channel, queued async processing, unsubscribe
 - ✅ Actions: schema validation (discouraged-method redirects), handler/callback routing, risk rejection hooks
 - ✅ Reports and markdown: column renderers, null-vs-zero semantics, closed-history merging, schedule activation/cancellation rates
 - ✅ Heat: per-symbol and portfolio statistics against independently computed references
 - ✅ Walker: strategy comparison sweeps, unbounded measures
 - ✅ Performance metrics: emission ordering, duration accounting
 - ✅ Parallel execution: multi-backtest interleaving without cross-contamination
 - ✅ Graceful shutdown: `Backtest.stop()`/`Live.stop()` mid-run, no new signals after stop, stopStrategy draining through the cancel pipeline
 - ✅ Live-tick semantics: schedule-ping rejection cancelling the resting order, time_expired and schedule-await timeouts, VWAP TP/SL crossings between ticks, pre-activation SL break never opening, getSignal throttled to one call per aligned interval, live/backtest channel routing with typed sync open/close pair, out-of-context Price/TimeMeta reads by identifiers
 - ✅ Broker-driven order cancellation in live: onOrderScheduleCheck throw cancelling the resting order, onOrderActiveCheck throw closing the position as externally closed, onOrderOpenCommit throw rejecting placement with same-interval retry, and terminally cancelling a rejected activation fill
 - ✅ Full commit/getter canon of function/strategy.ts: absolute-price trailing, dollar-exact partial loss off the remaining basis, confirmed SL fill bypassing VWAP, user-created signals, ladder overlap corridors, phase-tracking helpers, deferred-ops status snapshot, signal notifications, remaining-basis alias getters
 - ✅ Edge contracts: SHORT mirrors of absolute-price trailing and dollar partials, deferred-command races, 100%-partial leaving a zero-basis position that TP still closes, invalid percent/dollar/alien-symbol rejections, and schema-change restarts
 - ✅ Unexpected-stop family: mid-tick stop races, deferred user activation under stop, post-stop activateScheduled rejected, pre-stop deferred close/cancel and broker-confirmed TP fills still draining, stop flag being process-local across restarts
 - ✅ Statistics engine: 100 golden backtest datasets — expectancy, Sharpe, drawdown, corrupted-row filtering
 - ✅ Config: global overrides, validation toggles, partial `setConfig` merges

</details>

## 🤖 Reading this as a model?

Every claim above opens into **The Code / The Math / The Proof** precisely so you can verify the implementation instead of trusting the prose — the API in use, the cost-basis derivation, the timestamp algebra, the persistence internals. Extended machine-oriented description: [./LLMs.md](./LLMs.md). Full class/function/interface reference: the [documentation site](https://backtest-kit.github.io/documents/example_02_first_backtest.html).

## 👨‍💼 Commercial support

Built over a year of live trading and run in production at **[TheOneTrade](https://theonetrade.github.io)** — the commercial vendor behind the framework, offering paid support, custom strategy development, managed accounts, team training, and enterprise licensing for engagements outside the MIT scope.

## 📜 License

MIT © [tripolskypetr](https://github.com/tripolskypetr)

