fast_training_env

Attributes

EPSILON

PCT_TO_REWARD_SCALE

Classes

FastTrainingEnv

Fast training environment with minimal state tracking.

Module Contents

fast_training_env.EPSILON = 1e-08
fast_training_env.PCT_TO_REWARD_SCALE = 100.0
class fast_training_env.FastTrainingEnv(data, cfg, features, time_step=(TimeFrameUnit.Day, 1))

Bases: trading.src.alg.environments.base_environment.BaseTradingEnv

Fast training environment with minimal state tracking. Optimized for speed with constant-time operations. Does NOT maintain position history, trade constraints, or complex metrics. Target: 10,000 iterations per second.

Parameters:

  • data (pandas.DataFrame)

  • cfg (trading.cli.alg.config.StockEnv)

  • features (List[trading.src.features.generic_features.Feature])

  • time_step (tuple[alpaca.data.timeframe.TimeFrameUnit, int])

initial_cash
cash
holdings
_precompute_price_arrays()

Pre-compute price arrays and feature matrices for fast lookups.

_get_observation(i=-1)

Get observation with minimal computation using pre-computed matrices. Returns: [cash, holdings, current_prices, indicators]

Parameters:

i (int)

Return type:

numpy.ndarray

reset(*, seed=None, options=None)

Reset to initial state.

Parameters:

  • seed (Optional[int])

  • options (Optional[dict])

step(action)

Fast step with minimal state updates. Reward based on immediate portfolio value change.