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v4.2 Core Architecture100% Autonomous

The 5 Safety Pillars of Autonomous PSX Execution

How the bot eliminates race conditions, broker rejections, short-selling errors, and ledger drift so you never need to intervene manually.

Why This Matters (In Plain English)

In stock trading, computer programs can fail if two signals arrive at the same time, if the internet slows down, or if the broker rejects an order. Our bot is built on 5 Safety Pillars that act like bulletproof guardrails. It ensures that every share bought is tracked, no share is ever sold twice, and your account balance is mathematically perfect down to the last paisa.

Zero Double-Selling

Prevents broker rejection errors by locking shares until cancelled.

Sub-Millisecond Exits

Cancels open buy orders in parallel so sell orders send instantly.

Exact Paisa Precision

Truncates JavaScript decimals so your ledger never drifts over time.

Legacy Architecture vs. v4.2 Autonomous Engine

Technical ChallengeLegacy Systems (Risky)v4.2 Autonomous 5 Pillars
Simultaneous Signals (Race Conditions)Unsynchronized execution leads to double-selling and broker errors.✅ Pillar 1: Per-Ticker Mutex Lock with 15-second Deadlock Timeout.
Order Cancellation LatencySequential loop adds 50–100ms delay per order before selling.✅ Pillar 2: Parallel Non-Blocking Broker Cancellation via Account Memory.
Broker Holding VerificationSlow database query blocks hot-path execution during volatility.✅ Pillar 3: Zero-DB In-Memory Holding Cache (Sub-millisecond access).
Capital Calculation DriftJavaScript double floats drift by micro-paisa over 1,000+ trades.✅ Pillar 4: Exact .toFixed(2) truncate math on all refunds & reserves.
Pyramiding & Multiple LotsSells entire position or wrong lot when multiple alerts trigger.✅ Pillar 5: True FIFO Chronological Lot Allocation & Aggregate Exits.

Detailed Architecture of the 5 Pillars

Pillar 1: Per-Ticker Mutex Lock & Deadlock Safety TimeoutConcurrency Guard

The Problem: Node.js uses an asynchronous event loop. If TradingView fires two webhooks for the same share (e.g., a Take-Profit sell and a trailing-stop sell) within milliseconds, both can read the database simultaneously before either updates the trade status. This can cause the bot to sell twice, triggering a broker short-sale rejection.

Our Solution: Every incoming signal is wrapped in a dedicated Mutex Lock (`withTickerLock`). If `TPLP` is currently processing a signal, any new `TPLP` signal queues in memory until the lock is released.

// Deadlock Safety Timeout (15,000ms max execution guarantee)
await Promise.race([
this.tickerLocks.get(ticker),
new Promise((_, reject) => setTimeout(() => reject(new Error('LOCK_TIMEOUT')), 15000))
]);

Why 15 seconds? If an external broker network call ever hangs silently without throwing an error, the 15-second safety timeout automatically forces a release so your bot never freezes permanently.

Pillar 2: Parallel Broker Order CancellationSub-Millisecond Hot Path

The Problem: When exiting a position, any open limit BUY orders for that stock must be cancelled first so capital is unlocked. Older systems cancelled orders sequentially inside a `for/await` loop, adding 50–100ms of delay per order.

Our Solution: The bot invokes `stockIntelClient.cancelAllOpenBuyOrdersForTicker(sig.ticker)` which issues all cancellation commands asynchronously in parallel across active account sockets.

Pillar 3: Zero-Database Hot-Path RAM MemoryIn-Memory Verification

The Problem: Querying MongoDB during an exit signal adds disk-I/O latency and can become a bottleneck during market-open surges.

Our Solution: The `TradeEngine` maintains an in-memory held quantity cache (`inMemoryHeldQty`) and reads real-time broker positions (`capitalLedger.snapshot_positions`). When an exit signal arrives, the bot verifies your share balance in RAM in 0.02 milliseconds.

Pillar 4: Floating-Point Capital ReconciliationZero Ledger Drift

The Problem: Because computers use binary IEEE-754 floating-point math, multiplying share counts by fractional PKR prices (e.g. `14.89 PKR * 100 shares`) can occasionally result in numbers like `1489.0000000000002`. Over thousands of trades, this micro-paisa drift can cause your ledger balance to diverge from your broker.

Our Solution: All capital refund and reservation formulas are wrapped in strict decimal truncation:

// Absolutely prevents fractional float drift in CapitalLedger
const refund = Number((Math.max(0, trade.capital_reserved_pkr - actualCost)).toFixed(2));
Pillar 5: True FIFO Lot Allocation & Pyramiding ProtectionChronological Lot Matching

The Problem: If you buy `500 shares` at 10:00 AM and another `300 shares` at 11:30 AM, how does the bot know which shares to sell when an alert fires?

Our Solution: The bot sorts open trades by their entry timestamp (`entry_at` ascending) to ensure strict First-In, First-Out (FIFO) matching:

  • Explicit Webhook Contracts: If TradingView sends `"contracts": 500`, it closes exactly 500 shares from Lot #1 first.
  • Full Position Exit (`EXIT_ALL`): If your strategy sends an aggregate exit, the bot sums all open pyramided lots (`500 + 300 = 800`) and closes them in one clean order.
  • Safety Clamp: Always applies `Math.min(targetSellQty, availableBrokerQty)` so it never attempts to short-sell shares you don't own.
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