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train_complete_net.py
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import os
from argparse import ArgumentParser
import pandas as pd
import torch
from data_utils import (_cut_special, create_stocks_df,
create_synthetic_future_df, cut_df, filter_assets,
get_total_df_from_candles, read_all_candles)
from deepdow.callbacks import EarlyStoppingCallback, MLFlowCallback
from opt_nets import (EconomistNet, ResampleNetwork,
build_external_dataloaders, get_dataloaders,
get_predictions, get_test_dataloader,
get_variables_for_training, get_weights_submission,
train_model)
from submission_utils import (_convert_daily_submission, _weights_to_dict,
general_weights_fixer, get_submission_markowitz,
test_submission)
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
def create_optimizer(network, opt_type, lr):
if opt_type == "adam":
return torch.optim.Adam(network.parameters(), lr)
else:
return None
def _filter_submission(submission, first="2020-08-18", last="2020-12-24"):
first = pd.to_datetime(first)
last = pd.to_datetime(last)
after_first = submission.index >= first
before_last = submission.index <= last
between = after_first & before_last
return submission.loc[between]
def save_network(experiment_name, run, direc="models_2403"):
os.makedirs(direc, exist_ok=True)
network = run.models["main"]
torch.save(network.state_dict(), f"{direc}/{experiment_name}.pt")
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument(
"--lookback",
type=int,
required=False,
default=24,
help="Number of timesteps to look back",
)
parser.add_argument(
"--gap",
default=1,
type=int,
required=False,
help="Number of timesteps to leave before predicting",
)
parser.add_argument(
"--horizon",
default=24,
type=int,
required=False,
help="Number of timesteps to predict ahead.",
)
parser.add_argument(
"--optimizer", required=False, default="adam", type=str, help="Optimizer to use"
)
parser.add_argument(
"--lr", required=False, default=0.001, type=float, help="Learning rate to use."
)
parser.add_argument(
"--run_name",
required=False,
default="resamplenet_experiment",
type=str,
help="The number to put to the run.",
)
parser.add_argument(
"--epochs",
type=int,
required=False,
default=100,
help="Number of epochs to train",
)
parser.add_argument(
"--save_name",
default="prueba.csv",
type=str,
required=False,
help="Name to put to submission",
)
args = parser.parse_args()
os.makedirs("mlflow_runs", exist_ok=True)
candles = read_all_candles()
submission_muestra = pd.read_csv(
"submission/submission.csv", parse_dates=True, index_col="eod_ts"
)
# cols_ = [col.replace("allo_", "") for col in submission_muestra.columns]
# candles = {k:v for k, v in candles.items() if k in cols_}
# remove_assets = filter_assets(candles)
# candles = {k: v for k, v in candles.items() if k not in remove_assets}
df = get_total_df_from_candles(candles, add_random=True)
df = df.resample("H").aggregate("mean")
df_future_help = df.copy()
# df = _cut_special(df, first="2020-02-01")
stocks_df = create_stocks_df()
stocks_df.fillna(method="ffill", inplace=True)
stocks_df = _cut_special(stocks_df, first="2018-12-19", last="2020-06-17")
# stocks_df = _cut_special(stocks_df, first="2020-02-01", last="2020-06-17")
stocks_df.fillna(method="bfill", inplace=True)
# stocks_df = cut_df(stocks_df, df,)
n_timesteps, n_channels, n_assets, asset_names = get_variables_for_training(df)
train_dataloader, val_dataloaders, dataset = build_external_dataloaders(
stocks_df, df, args.lookback, args.gap, args.horizon, perc_val=0.25
)
network = EconomistNet(
n_external=len(stocks_df.columns.levels[0]),
n_assets=len(df.columns.levels[0]),
max_weight=0.10,
)
run = train_model(
network,
train_dataloader,
val_dataloaders,
create_optimizer(network, args.optimizer, args.lr),
callbacks=[
MLFlowCallback(
args.run_name,
mlflow_path="./mlflow_runs",
experiment_name="economistnet",
log_benchmarks=True,
),
EarlyStoppingCallback("val", "loss"),
],
epochs=args.epochs,
device="cuda",
loss_="maximum_drawdown"
)
save_network("complete_2703", run, direc="models_2703")
submission_df = pd.read_csv(
"/home/alejandro.vaca/reto_series_temporales/submission/submission.csv",
parse_dates=True,
index_col="eod_ts",
)
# future_df = create_synthetic_future_df(candles, submission_df)
stocks_test = create_stocks_df()
stocks_test = stocks_test.resample("D").aggregate("mean").ffill()
stocks_test = _cut_special(
stocks_test, first="2020-05-01", last="2021-03-15"
) # "2020-07-25")
stocks_hourly = stocks_test.resample("H").aggregate("mean")
index = pd.date_range(start="2020-05-01", end="2021-03-15", freq="H")
future_df = df_future_help.sample(stocks_hourly.shape[0]).copy().set_index(index)
# n_timesteps, n_channels, n_assets, asset_names = get_variables_for_training(future_df)
# future_df = future_df.resample("H").aggregate("mean")
test_dataloader = get_test_dataloader(
stocks_test, future_df, args.lookback, args.gap, args.horizon
)
submission = get_weights_submission(test_dataloader, run.models["main"])
submission = _filter_submission(submission)
submission.index = submission.index.rename("eod_ts")
submission = _convert_daily_submission(submission, submission_muestra)
submission.to_csv(args.save_name, index=True, header=True)