The End-to-End Research-to-Execution Pipeline for Systematic Trading
Connect unstructured financial data, corporate filings, and machine learning models directly to broker APIs.
Why Standard Signal Pipelines Break Down in Live Trading
Most systematic trading stacks are stitched together from research notebooks, alert services and broker SDKs. They work in a backtest and fail under live market conditions at three predictable points.
Unstructured Data Ingestion Latency
Corporate filings, exchange announcements and news arrive as PDFs, HTML and free text. Manual or batch parsing adds minutes to hours between the information event and a tradable signal, by which time the edge has decayed.
- ✓Polling-based scrapers miss or duplicate events
- ✓No normalized timestamps across sources
- ✓PDF and table parsing is brittle
LLM Hallucination Risk in Live Trading
Large language models are effective at reading filings, but free-form output can invent tickers, misread numbers or flip sentiment. When that output feeds an order router directly, a single hallucination becomes a real position.
- ✓Unvalidated free-text responses
- ✓Fabricated symbols and quantities
- ✓Non-deterministic decisions with no audit trail
Execution Disconnected from Real-Time Margin
Alert-to-order bridges typically fire orders without checking available margin, open exposure or lot-size rules. The result is rejected orders, partial hedges, and leveraged positions that breach the trader's risk plan.
- ✓No pre-trade margin verification
- ✓Orphaned legs on multi-leg F&O strategies
- ✓No reconciliation against the broker order book
From Raw Data to Reconciled Fill in Six Deterministic Stages
Each stage has a single responsibility, a typed contract with the next stage, and its own logging. Any stage can reject an event; only stage 5 can talk to a broker.
Ingestion Engine
Collects and normalizes every input into a timestamped, de-duplicated event stream.
- ✓Corporate filings and exchange announcements (NSE, BSE, SEC EDGAR)
- ✓TradingView and custom webhooks
- ✓News feeds and RSS sources
- ✓PDF, HTML and table parsing
LLM Extraction Layer
Turns unstructured text into a structured JSON schema. The model reads; it never decides or trades.
- ✓Fixed output schema: ticker, event type, direction, confidence, source
- ✓Pydantic validation on every response
- ✓Symbol checks against the instrument master
- ✓Invalid output rejected and logged
Signal Validation
Quantitative models decide whether a structured event is tradable.
- ✓Backtested entry thresholds
- ✓Volatility and market-regime filters
- ✓Liquidity and spread checks
- ✓Signal de-duplication and cooldowns
Deterministic Risk Gate
Rule-based code that approves, resizes or blocks every order before it exists.
- ✓Position sizing by capital at risk and lot size
- ✓Per-symbol and portfolio exposure caps
- ✓Daily max-loss and drawdown limits
- ✓Global and per-strategy kill switches
Broker Execution
The only component with broker credentials. Places, modifies and tracks orders.
- ✓Zerodha Kite Connect
- ✓DhanHQ API
- ✓Interactive Brokers (IBKR) API
- ✓Idempotent order IDs, retries and order-state tracking
Telemetry & Reconciliation
Proves what happened, when, and why, for every event and every order.
- ✓Immutable audit logs from source event to fill
- ✓Position and fill reconciliation with the broker
- ✓Latency and rejection monitoring
- ✓Real-time alerts on drift or failures
The LLM Never Touches the Broker API
Capital protection is enforced by architecture, not by prompts. The language model is isolated in the extraction layer with no credentials and no network path to a broker. It can only emit a schema-validated JSON object. Every order is created by deterministic, testable rules that produce the same decision for the same inputs.
Strict Pydantic Schema Enforcement
Every LLM response is parsed into a typed Pydantic model with required fields, enumerated event types, bounded confidence scores and validated instrument symbols. Extra fields, missing fields or out-of-range values raise a validation error, and the event is dropped and logged rather than passed downstream.
Real-Time Margin Verification
Before an order is created, the risk gate queries available funds and required margin from the broker, checks open exposure and lot-size rules, and resizes or blocks the order if limits would be breached. Orders are never sent on the assumption that margin is available.
Emergency Circuit Breakers
Kill switches halt new orders and optionally flatten positions when daily loss limits, drawdown thresholds, order-rejection rates or abnormal latency are hit. They can be triggered automatically or manually, and they apply globally or to a single strategy.
Deterministic Execution Rules
The LLM proposes structured facts. Quant models score them. Only rule-based code can approve an order. That makes every trade explainable, reproducible in testing, and traceable in the audit log.
Research-to-Execution Pipeline FAQ
Direct answers to the questions quants, trading educators and small funds ask most often.
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