Untitled

import logging

import click
import pandas as pd
from google.protobuf.wrappers_pb2 import DoubleValue

from tradewell.data.s3 import write_s3_table
from tradewell.util.cli import load_config, run_click_command
from tradewell.util.proto import now_header
from tradewell.util.pyarrow import log_table_stats, proto_records_to_table
from tradewell.util.tradewell_s3_file_system import create_file_system
from tradewell_proto.quote_pb2 import Quote

logger = logging.getLogger(__name__)
TABLE_NAME = "batch-example"


@click.command()
@click.option("--date", type=pd.Timestamp, required=True)
def batch_example(date: pd.Timestamp) -> None:
    config = load_config()
    file_system = create_file_system(config.env_bucket, config)
    
    # Load all your data

    # Train your model

    # Then you evaluate your model on the given 'date' to generate prices

    # Store those prices in a pandas dataframe
    
    # Convert your pandas dataframe into PyArrow Table

    df = gen_predictions_for_date(date)
    table = your_func_to_convert_pandas_df_to_pyarrow_table(df)
    log_table_stats(logger, TABLE_NAME, table)
    write_s3_table(table, TABLE_NAME, date, file_system)


if __name__ == "__main__":
    run_click_command(batch_example)