RL Alpha Labs

Problem

Trading ideas that look good in a backtest usually lose to an index fund once real costs, tax and dead companies are counted. The question the lab asks of every idea is whether it still wins after all of that.

Approach

• Honest data. Point-in-time NSE data, with delisted companies kept in, so the past is not filtered by who survived.
• Honest costs. Every statutory charge, depository fee, spread and the tax wedge, written down in the open.
• One bar. An idea counts only if it beats an index fund after costs and tax.
• Ideas of every size. From reinforcement-learning agents trained in JAX to simple rules.

Results

At least 12 ideas are closed, each with its reason. Holding a 300-stock basket directly lost 2.14% a year to costs and tax over twenty years, and the index fund came out ahead in 69 of 70 tiers of capital and breadth.

What did not work

Every closed idea is a thing that did not work, written down with why. Backtest traps fooled the lab or nearly did; each one is listed with the check that now catches it.

Ideas closed so far

Costs and taxes

• A hand-picked basket of large stocks instead of an index fund
• Intraday chart-pattern setups
• Order-book signals over seconds to minutes
• Trading other public filing feeds on their own

No edge, or only luck

• Reinforcement-learning agents trading on daily prices
• A bigger machine-learning model to rank stocks
• Buying after a big news-driven jump
• Commodity price shocks to predict company results
• Writing options on the index for income

The market got there first

• Buying stocks announced for addition to a major index

AI that fell short of the bar

• An AI model reading company filings to forecast earnings surprises
• An AI model mapping which companies supply which, from annual reports

From rlalphalabs.com, read 2026-10-28.