rust · reinforcement learning
rler
a rust reinforcement-learning paper-trading workbench for nse equities on 15-minute candles.
rust
reinforcement learning
q-learning
nse
paper trading
walk-forward
yew
mit
what it is
rler is a workbench for teaching an agent to trade on 15-minute candles — and, more honestly, for teaching me how reinforcement learning behaves on noisy financial data. it's paper-only. it does not place broker orders and it does not give investment advice.
the current rust implementation does tabular q-learning, csv and yahoo candle loading, backtests, paper replay, walk-forward validation with monte-carlo robustness, sqlite run history, and a rust/yew dashboard. it ships with sample data so it works fully offline. dqn is intentionally deferred — getting the tabular case honest comes first.
what each command does
→train — fit one tabular q-learning model
→backtest — train on earlier rows, evaluate on later rows
→paper — replay paper trading with a saved model
→validate — rolling walk-forward validation, the test that actually matters
rustlanguage
15mcandle resolution
offlineruns with sample data
mitlicense
project boundaries, stated up front: paper-trading only, no real broker execution, no profit guarantee, deep-rl is a future phase. honesty about scope is part of the design.
built by dharun ashokkumar