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feat(Events): added EventFilter, EventLabeller (#186)

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This commit is contained in:
Mark Aron Szulyovszky
2022-01-26 23:22:43 +01:00
committed by GitHub
parent 1042c82333
commit 42a1bc59cb
65 changed files with 759 additions and 276571 deletions
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run_pipeline.py
+25 -23
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@@ -1,27 +1,29 @@
import pandas as pd
from typing import Callable, Optional
from data_loader.load_data import load_data
from data_loader.process_data import check_data
from config.types import Config, RawConfig
from config.preprocess import preprocess_config, validate_config
from config.presets import get_default_ensemble_config, get_lightweight_ensemble_config
from data_loader.load import load_data
from data_loader.process import check_data
from labeling.process import label_data
from reporting.wandb import launch_wandb, override_config_with_wandb_values
from reporting.reporting import report_results
from reporting.saving import save_models
from config.config import Config, get_default_ensemble_config, get_lightweight_ensemble_config
from config.preprocess import validate_config, preprocess_config
from reporting.types import Reporting
from training.training_steps import primary_step, secondary_step
from reporting.types import Reporting
import ray
ray.init()
def run_pipeline(project_name:str, with_wandb: bool, sweep: bool, get_config: Callable) -> tuple[Reporting.Asset, Config, pd.DataFrame, pd.DataFrame, pd.DataFrame]:
wandb, config = __setup_config(project_name, with_wandb, sweep, get_config)
def run_pipeline(project_name:str, with_wandb: bool, sweep: bool, raw_config: RawConfig) -> tuple[Reporting.Asset, Config, pd.DataFrame, pd.DataFrame, pd.DataFrame]:
wandb, config = __setup_config(project_name, with_wandb, sweep, raw_config)
reporting = __run_training(config)
results, all_predictions, all_probabilities, all_models = reporting.get_results()
report_results(results, all_predictions, config, wandb, sweep, project_name)
@@ -30,8 +32,7 @@ def run_pipeline(project_name:str, with_wandb: bool, sweep: bool, get_config: Ca
return all_models, config, results, all_predictions, all_probabilities
def __setup_config(project_name:str, with_wandb: bool, sweep: bool, get_config: Callable) -> tuple[Optional[object], Config]:
raw_config = get_config()
def __setup_config(project_name:str, with_wandb: bool, sweep: bool, raw_config: RawConfig) -> tuple[Optional[object], Config]:
wandb = None
if with_wandb:
wandb = launch_wandb(project_name=project_name, default_config=raw_config, sweep=sweep)
@@ -48,29 +49,30 @@ def __run_training(config: Config):
reporting = Reporting()
# 1. Load data, check for validity
X, y, target_returns = load_data(
X, returns, forward_returns = load_data(
assets = config.assets,
other_assets = config.other_assets,
exogenous_data = config.exogenous_data,
target_asset = config.target_asset,
load_non_target_asset = config.load_non_target_asset,
log_returns = config.log_returns,
forecasting_horizon = config.forecasting_horizon,
own_features = config.own_features,
other_features = config.other_features,
exogenous_features = config.exogenous_features,
no_of_classes = config.no_of_classes,
)
assert check_data(X, y, config) == True, "Data is not valid."
# 2. Train a Primary model with optional metalabeling for each asset
training_step_primary, current_predictions = primary_step(X, y, target_returns, config, reporting, from_index = None)
assert check_data(X, config) == True, "Data is not valid."
# 2. Filter for significant events when we want to trade, and label data
events, X, y, forward_returns = label_data(config.event_filter, config.labeling, X, returns, forward_returns)
# 3. Train a Primary model with optional metalabeling for each asset
training_step_primary, current_predictions = primary_step(X, y, forward_returns, config, reporting, from_index = None)
# 3. Train an Ensemble model with optional metalabeling for each asset
training_step_secondary = secondary_step(X, y, current_predictions, target_returns, config, reporting, from_index = None)
# 4. Train an Ensemble model with optional metalabeling for each asset
training_step_secondary = secondary_step(X, y, current_predictions, forward_returns, config, reporting, from_index = None)
# 4. Save the models
reporting.asset = Reporting.Asset(ticker= config.target_asset[1], primary=training_step_primary, secondary=training_step_secondary)
# 5. Save the models
reporting.asset = Reporting.Asset(name = config.target_asset[1], primary = training_step_primary, secondary = training_step_secondary)
return reporting
@@ -78,4 +80,4 @@ def __run_training(config: Config):
if __name__ == '__main__':
run_pipeline(project_name='price-prediction', with_wandb = False, sweep = False, get_config=get_default_ensemble_config)
run_pipeline(project_name='price-prediction', with_wandb = False, sweep = False, raw_config=get_default_ensemble_config())