Run the pipeline

After verifying single strategy run and exploring logs, let's run the full pipeline with backtesting and slide window backtets on multiple parametrs.

Full code of this example can be found in our repo.

import warnings
warnings.filterwarnings('ignore')

from typing import List

import numpy as np

from sklearn.model_selection import ParameterGrid

from fractal.loaders import BinanceDayPriceLoader, LoaderType
from fractal.core.base import Observation
from fractal.core.pipeline import (
    DefaultPipeline, MLFlowConfig, ExperimentConfig)

from binance_entity import BinanceGlobalState
from holder_strategy import BinanceHodlerStrategy


# Load prices from Binance and build observations
def build_observations() -> List[Observation]:
    # Load prices from Binance
    binance_prices = BinanceDayPriceLoader('BTCUSDT', loader_type=LoaderType.CSV).read(with_run=True)

    # Build observations list
    observations: List[Observation] = [
        Observation(timestamp=timestamp, states={'exchange': BinanceGlobalState(price=price)})
        for timestamp, price in zip(binance_prices.index, binance_prices['price'])
    ]
    return observations


# Build a grid of parameters to search
def build_grid() -> ParameterGrid:
    grid = ParameterGrid({
        'BUY_PRICE': np.linspace(50_000, 60_000, 3),
        'SELL_PRICE': np.linspace(60_000, 70_000, 3),
        'TRADE_SHARE': np.linspace(0.1, 0.9, 3),
        'INITIAL_BALANCE': [100_000]
    })
    return grid


if __name__ == '__main__':
    # Define MLFlow and Experiment configurations
    mlflow_config: MLFlowConfig = MLFlowConfig(
        mlflow_uri='http://127.0.01:5000',
        experiment_name='binance_hodler_btc_0'
    )
    experiment_config: ExperimentConfig = ExperimentConfig(
        strategy_type=BinanceHodlerStrategy,
        fractal_observations=build_observations(),
        window_size=24,
        params_grid=build_grid(),
        debug=True,
    )
    pipeline: DefaultPipeline = DefaultPipeline(
        experiment_config=experiment_config,
        mlflow_config=mlflow_config
    )
    pipeline.run()