CoinMarketCap.py

coinmarketDataset.py

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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