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Mixtral_ether

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Mixtral_ether
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import os
import logging
import pandas as pd
import numpy as np
from datetime import datetime
from .creator import create_dataset

logger = logging.getLogger(__name__)


class CoinMarketDataset:
    dataset = []

    def __init__(self, main_features, start_date=None, end_date=None, window_size=10):
        import requests

        # Fetching data from the server
        url = "https://web-api.coinmarketcap.com/v1/cryptocurrency/ohlcv/historical"
        # param = {"convert":"USD","slug":"bitcoin","time_end":"1601510400","time_start":"1367107200"}
        param = {"convert": "USD", "slug": "bitcoin", "time_end": "1672384689", "time_start": "1367107200"}
        content = requests.get(url=url, params=param).json()
        df = pd.json_normalize(content['data']['quotes'])

        # Extracting and renaming the important variables
        df['Date'] = pd.to_datetime(df['quote.USD.timestamp']).dt.tz_localize(None)
        df['Low'] = df['quote.USD.low']
        df['High'] = df['quote.USD.high']
        df['Open'] = df['quote.USD.open']
        df['Close'] = df['quote.USD.close']
        df['Volume'] = df['quote.USD.volume']

        # Drop original and redundant columns
        df = df.drop(columns=['time_open', 'time_close', 'time_high', 'time_low', 'quote.USD.low', 'quote.USD.high',
                              'quote.USD.open', 'quote.USD.close', 'quote.USD.volume', 'quote.USD.market_cap',
                              'quote.USD.timestamp'])

        # Creating a new feature for better representing day-wise values
        df['Mean'] = (df['Low'] + df['High']) / 2

        # Cleaning the data for any NaN or Null fields
        df = df.dropna()

        # Creating a copy for making small changes
        dataset_for_prediction = df.copy()
        # print(dataset_for_prediction.keys())
        dataset_for_prediction['Actual'] = dataset_for_prediction['Mean'].shift()
        dataset_for_prediction = dataset_for_prediction.dropna()

        # date time typecast
        dataset_for_prediction['Date'] = pd.to_datetime(dataset_for_prediction['Date'])
        dataset_for_prediction.index = dataset_for_prediction['Date']

        drop_cols = ['High', 'Low', 'Close', 'Open', 'Volume', 'Mean']
        for item in main_features:
            if item in drop_cols:
                drop_cols.remove(item)
        df = df.drop(drop_cols, axis=1)

        if start_date == '-1':
            start_date = df.iloc[0].Date
        else:
            start_date = datetime.strptime(str(start_date), '%Y-%m-%d %H:%M:%S')

        if end_date == '-1':
            end_date = df.iloc[-1].Date
        else:
            end_date = datetime.strptime(str(end_date), '%Y-%m-%d %H:%M:%S')

        start_index = 0
        end_index = df.shape[0] - 1
        for i in range(df.shape[0]):
            if df.Date[i] <= start_date:
                start_index = i

        for i in range(df.shape[0] - 1, -1, -1):
            if df.Date[i] >= end_date:
                end_index = i

        # prediction mean based upon open
        dates = df.Date[start_index:end_index]
        df = df.drop('Date', axis=1)
        arr = np.array(df)
        arr = arr[start_index:end_index]
        features = df.columns

        self.dataset, self.profit_calculator = create_dataset(arr, list(dates), look_back=window_size, features=features)


    def get_dataset(self):
        return self.dataset, self.profit_calculator