cache_data.py

import requests
import pandas as pd
import time
import json

URL = "https://raw.githubusercontent.com/bitkarrot/satshkd-vercel/main/public/hkd_historical"

cache_data = {}
refresh_interval = 24 * 60 * 60  # 24 hours in seconds


def format_df(df):    
    df["date"] = pd.to_datetime(df["date"])
    df["btcusd_rate"] = pd.to_numeric(df["btcusd_rate"])
    df["sathkd_rate"] = pd.to_numeric(df["sathkd_rate"])
    df["btchkd_rate"] = pd.to_numeric(df["btchkd_rate"])
    df["usdsat_rate"] = pd.to_numeric(df["usdsat_rate"])

    df = df.set_index("date")
    return df


def get_cached_data():
    # Check if cached data exists and is less than 24 hours old
    if (
        "data" in cache_data
        and time.time() - cache_data["timestamp"] < refresh_interval
    ):
        # Load data from cache
        df = pd.DataFrame(cache_data["data"])
        print(df.dtypes)

    else:
        # Fetch data from URL
        response = requests.get(URL)
        data = response.json()

        # Convert data to dataframe and save to cache
        df = pd.DataFrame(data)
        cache_data["data"] = df.to_dict("records")
        cache_data["timestamp"] = time.time()

    # Use the dataframe as needed
    return format_df(df)


def get_data_from_file(datafile):
    data = None
    with open(datafile, "+r") as f:
        raw = f.read()
        data = json.loads(raw)
        df = pd.json_normalize(data)
        return format_df(df)


if __name__ == "__main__":
    # df = get_data()
    # print(df.head())
    # print(df.dtypes)
    datafile = "./btc_historical"
    dfile = get_data_from_file(datafile)
    print(dfile.head())
    print(dfile.dtypes)

    df_weekly = dfile.resample("W").last()
    print(df_weekly)

    df_biweekly = dfile.resample("2W").last()
    print(df_biweekly)

    df_monthly = dfile.resample("M").last()
    print(df_monthly)

    print(dfile.index)