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AI4Finance-Foundation
GitHub Repository: AI4Finance-Foundation/FinRL
 Path: blob/master/finrl/meta/preprocessor/example_of_shioaji_api.py
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from __future__ import annotations

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import gc

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import pandas as pd

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from finrl.config_tickers import TAI_0050_TICKER

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from finrl.meta.data_processors.processor_sinopac import SinopacProcessor

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from finrl.meta.preprocessor.preprocessors import data_split

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from finrl.meta.preprocessor.shioajidownloader import SinopacDownloader

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TRAIN_START_DATE = "2023-04-13"

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TRAIN_END_DATE = "2024-04-13"

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TRADE_START_DATE = "2024-04-13"

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TRADE_END_DATE = "2024-07-31"

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def process_ticker_data(ticker):

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    print(f"Processing data for ticker: {ticker}")

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    df_raw = SinopacDownloader(

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        start_date=TRAIN_START_DATE, end_date=TRADE_END_DATE, ticker_list=[ticker]

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    ).fetch_data()

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    df_raw.rename(

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        columns={

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            "open": "Open",

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            "high": "High",

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            "low": "Low",

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            "close": "Close",

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            "volume": "Volume",

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            "amount": "Amount",

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        },

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        inplace=True,

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    )

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    processor = SinopacProcessor(

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        API_KEY="3Tn2BbtCzbaU1KSy8yyqLa4m7LEJJyhkRCDrK2nknbcu",

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        API_SECRET="Epakqh1Nt4inC3hsqowE2XjwQicPNzswkuLjtzj2WKpR",

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    )

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    cleaned_df = processor.clean_data(df_raw)

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    df_with_indicators = processor.add_technical_indicator(cleaned_df)

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    df_with_vix = processor.add_vix(df_with_indicators)

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    df_with_turbulence = processor.add_turbulence(df_with_vix, time_period=252)

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    # Save processed data for each ticker to a separate file

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    df_with_turbulence.to_csv(f"data_{ticker}.csv")

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    # Explicitly delete unused objects and collect garbage

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    del df_raw, cleaned_df, df_with_indicators, df_with_vix, df_with_turbulence

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    gc.collect()

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df_final = pd.DataFrame()

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for ticker in TAI_0050_TICKER:

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    process_ticker_data(ticker)

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    # Load processed data from file and concatenate

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    df_ticker = pd.read_csv(f"data_{ticker}.csv")

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    df_final = pd.concat([df_final, df_ticker], ignore_index=True)

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    del df_ticker  # free up memory

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    gc.collect()

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train = data_split(df_final, TRAIN_START_DATE, TRAIN_END_DATE)

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trade = data_split(df_final, TRADE_START_DATE, TRADE_END_DATE)

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train.to_csv("train_data.csv")

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trade.to_csv("trade_data.csv")

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