An institutional-grade AI trading platform for NSE & BSE. Rigorously validated — not just backtested. Currently in research. Not claiming live alpha yet.
Building a strategy that looks great in backtests is easy. Making it survive live markets is not.
A model optimized on historical data captures noise, not signal. Without walk-forward validation, backtest performance is fiction.
Ignoring NSE transaction taxes (STT, stamp duty, exchange fees), slippage, and minimum lot sizes inflates paper returns by 30–50%.
Training RL agents for days per experiment destroys research velocity. You can't build intuition about what works without rapid feedback.
Agents that maximize raw returns without drawdown constraints become reckless. Risk must be part of the objective, not an afterthought.
Deep reinforcement learning agents trained on real NSE data with realistic transaction costs and risk constraints.
Multi-window rolling validation. The agent must generalize across time periods — not just memorize one regime.
XLA-compiled training loops for rapid iteration. Experiments complete in minutes, not days.
Full Indian brokerage cost model: STT, exchange fees, SEBI charges, GST, stamp duty. Slippage and market impact included.
Extensive coverage across timeframes and indices. From minute bars to daily, spanning large-cap to small-cap universes.
Multi-objective reward function combining returns, drawdown penalties, concentration limits, and turnover costs. Agent learns risk discipline, not just return maximization.
Not synthetic data. Historical data fetched directly via broker API, stored in columnar format with quality validation.
An agent only advances when it clears measurable criteria. No exceptions. This is what separates research-grade from production-grade.
Extensive NSE/BSE coverage across multiple granularities via broker API. Checkpointed parallel downloads, quality validation, multi-source fallback.
Technical indicators and auxiliary signal models for regime detection, volatility forecasting, and trend classification.
Agents trained with NSE-realistic frictions and strict promotion gates. Only agents that clear all criteria advance to the next stage.
Multi-window rolling test proving the agent generalizes — not just memorizes one period. Adversarial stress tests under extreme market conditions.
30+ day live simulation against real market data. Realistic order fills, portfolio tracking, real-time P&L. An agent that cannot pass paper trading does not go live.
Low-latency inference via broker API. Live risk controls: position limits, drawdown circuit breakers, concentration guards. Real capital only after validation and paper trading clear.
These numbers come directly from experiment records. Test metrics are on held-out data the agent never saw during training. Past simulation results do not guarantee future live performance.
Generated using real test statistics from held-out evaluation.
Curve parameterised from real test statistics, not cherry-picked. Benchmark uses estimated NIFTY 50 historical parameters. This is one representative episode from multi-episode evaluation — not the best one.
Transparency is a feature. Here is the current development state, without spin.
We are a research-stage project. No real capital is deployed. We are seeking collaborators and investors who understand the difference between a validated research prototype and a live product — and who see the value in what's been built so far.
Real experiment IDs, real timestamps, real metrics from disk. Every run is reproducible from its checkpoint.
Selected metrics from representative training runs. All results on held-out test data the agent never saw during training.
Building production-grade reinforcement learning systems for Indian equities from first principles. Background spans machine learning research, systems engineering, and quantitative analysis. This project exists because RL for Indian markets is severely underexplored — and because most "AI trading" products are backtested fiction. I believe rigorous validation, honest metrics, and realistic cost models are the only path to real alpha.
Early-stage opportunity in a validated RL trading research platform built for Indian equities. Pipeline is clear. Risk gates are real. Results published, more in pipeline.
Looking for a serious collaborator on RL for Indian markets? The infrastructure is built. The hard problems remaining are algorithmic: walk-forward consistency, multi-agent coordination, curriculum learning.
Interested in contributing to a production-bound RL system? Phases 3–6 of the roadmap involve architecture consolidation, AutoRL, and NIFTY 100+ universe expansion.
Need an AI trading engine for Indian equities that you can run in-house? This is modular: swap broker backends, risk profiles, and asset universes. Licensing and white-label options available post-Stage 5.
No pitch decks sent cold. If you've read this far, you understand what's been built and what stage it's at. That's the kind of conversation worth having.