How does TraderOps guarantee backtest-to-live parity?

Trigger prices are anchored to the strategy's intended entry, so the SL/target/trailing ladder is identical across backtest, paper, and live. Slippage moves only the fill, keeping results paisa-exact.

Which brokers does TraderOps support?

Zerodha, AliceBlue, Tradejini, Zebu, Dhan, and Delta, via a broker-agnostic capability layer.

Can I build strategies without coding?

Yes — ORB, WAT, re-entry, and multi-leg option strategies are composed through configuration and controllable from Telegram or an MCP client.

What kinds of case studies are published here?

Deep-dives on AI agents we have shipped: voice AI for telecalling, dental, real-estate, and travel concierge use cases; multi-modal chatbots; content-automation pipelines; cross-cultural tone-checkers; Reddit lead-capture; and ScrapCRM. Each walks through architecture, decisions, and measurable outcomes.

How are case studies different from blog posts?

Case studies are anchored to a specific shipped product with a real client, real metrics, and a real testimonial. Blog posts are commentary, opinions, and engineering notes that are not tied to a single project.

What stack do you typically use for AI agents?

It depends on the constraints. Common picks: ElevenLabs or Twilio for voice; OpenAI, Anthropic, or open-weight models for the LLM layer; n8n or custom orchestration for the agent loop; Postgres and Redis for state; plus the integration layer (CRMs, calendars, telephony) tailored per project. Each case study lists the exact stack.

Can I use these as a buying signal for my own AI build?

Yes — that is exactly what they are for. Each one names the problem we were solving, the architecture we chose and why, and the measurable result. If your situation rhymes with one of them, that is a strong indicator we can help.

How do I start a conversation about a similar build?

Email contact@thworks.org with the case study that is closest to what you are trying to ship, plus your top three unknowns. We will book a 30-minute scoping call and return with a concrete plan, timeline, and price.

TraderOps trading operating system showing the no-code strategy builder, multi-broker execution routing, and live P&L monitoring.

All Case Studies

FinTech & Algorithmic TradingFinTech EngineeringTrading InfrastructureAI & Automation

TraderOps: A Multi-Broker Trading OS with Paisa-Exact Backtest-to-Live Parity

Unified strategy building, backtesting, and live execution on one engine — 6 broker integrations, no-code strategies, and Telegram + MCP control with kill-switch safety.

6Broker Integrations

Paisa-exactBacktest↔Live Parity

3Execution Modes

Web·Telegram·MCPControl Surfaces

THWORKS built TraderOps — 'the trader's operating system' — that lets traders learn, test, and trade a strategy on a single engine. Users compose ORB, WAT, re-entry, and multi-leg option strategies without code, validate them on historical data with paisa-exact trigger alignment to live execution, then route orders across six brokers (Zerodha, AliceBlue, Tradejini, Zebu, Dhan, Delta). A hybrid stop-loss combines broker-side orders with real-time monitoring, and the engine is controllable from Telegram and any MCP client — all protected by kill switches, per-strategy capital limits, and daily position guards.

The Challenge: Backtests That Never Survive Contact With the Live Market

Traders run execution in a broker terminal, research in a disconnected backtester whose fills never match reality, and risk management in spreadsheets. Strategies that look profitable in a backtest collapse live because trigger math, slippage, and order-type behaviour differ. Managing stop-losses by hand across multiple broker accounts is error-prone, and there is no safe way to kill everything when a day goes wrong.

Disciplined, salaried traders want trustworthy automation without writing code or babysitting a screen. The goal was one engine where a strategy is authored once and behaves identically in paper and live — so what you test is exactly what you trade — while staying broker-agnostic and safe by construction.

Our Solution: One Engine, Broker-Agnostic, Paisa-Exact

TraderOps is a Python/FastAPI engine over PostgreSQL with a broker-abstraction layer that normalises order placement, modification, and reconciliation across every broker. SL/target/trailing trigger prices are anchored to the strategy's intent, so the trigger ladder is bit-exact across live, paper, and backtest by construction — slippage moves the fill, never the trigger.

A single-orderbook reconcile loop is the only path that updates trade state from the broker, removing race conditions between per-order polling and event handling. Market data flows from one shared platform feed rather than per-user tickers, and execution is decoupled from the data source so any broker executes against a common feed. Every action — Telegram command, MCP call, webhook, or scheduler — lands in the same audited trade pipeline.

Key Technical Decisions

Paisa-exact parity: trigger prices anchored to expected entry give an identical SL/target/trailing ladder across backtest, paper, and live — the platform's core trust guarantee.

Broker-agnostic capability layer: a frozen per-broker model (tag format, product/exchange maps, order semantics) means engine code never branches on broker name, so adding a broker is additive and safe.

Control anywhere: a read/write MCP server plus a Telegram bot let traders author, fire, and monitor strategies conversationally, behind a two-step confirm gate on every order-placing action.

Safe by construction: mode-scoped kill switches, per-strategy capital limits, and a scheduled MIS auto-square-off protect capital with no manual intervention.

Results: What You Test Is Exactly What You Trade

Brokers Supported

Execution Modes

Paisa-exact

Backtest↔Live

Before

A broker terminal for orders, a mismatched backtester for research, and spreadsheets for risk — with results that never held up live and stop-losses managed by hand across accounts.

After

One engine where a no-code strategy is authored once, validated with paisa-exact parity, and executed live across six brokers — controlled from Telegram or an AI client, with automated stop-losses and kill-switch safety.

Technology Stack

Python / FastAPIPowers the async execution engine, scheduler, and shared REST API behind every client — web, Telegram, MCP, and webhooks.

PostgreSQL + SQLAlchemyTransactional source of truth for trades, strategy runs, and audit events, continuously reconciled against the broker.

Model Context Protocol (MCP)Exposes read + write trading tools to AI clients so strategies can be authored, fired, and monitored conversationally behind a confirm gate.

Telegram Bot APILets traders run, monitor, and exit strategies from chat with rich fill and P&L notifications — no screen required.

Multi-Broker AdaptersZerodha, AliceBlue, Tradejini, Zebu, Dhan, and Delta behind one interface so a strategy runs on any of them.

"TraderOps finally closed the gap between my backtest and my live trades. What I test is exactly what fires — down to the paisa — and I can manage everything from Telegram without staring at a screen all day."

Private Beta TraderOptions Trader, TraderOps Early Access

Frequently Asked Questions

Common questions about this project and our approach.

SL, target, and trailing trigger prices are anchored to the strategy's intended entry rather than the actual fill, so the trigger ladder is computed identically in backtest, paper, and live. Slippage moves only the fill price — never the trigger — keeping results paisa-exact across modes.