Update app.py

app.py

@@ -54,33 +54,28 @@ class TradingEnv(gym.Env):
 
         return self._get_observation(), reward, done, {}
     
-def fetch_data(symbol='ETHUSDT', interval='1h', start_date='2021-01-01'):
-    end_date = datetime.utcnow()
+def fetch_data(symbol='ETH', tsym='USD', start_date='2021-01-01', api_key='66bc686cb714fadda1fad0320704c98869d4b31ce7d9d27560c6c574b4d04c54'):
     start_date = datetime.strptime(start_date, '%Y-%m-%d')
-    klines = []
+    end_date = datetime.utcnow()
+    to_ts = int(end_date.timestamp())
     
-    while start_date < end_date:
-        url = f'https://api.binance.com/api/v3/klines?symbol={symbol}&interval={interval}&startTime={int(start_date.timestamp() * 1000)}'
-        response = requests.get(url)
-        if response.status_code != 200:
-            print(f"Error fetching data: {response.status_code} {response.text}")
-            break
-        data = response.json()
-        if not data:
-            print("Fetched data is empty. Please check the API request and parameters.")
-            break
-        else:
-            print("Fetched data successfully.")
-        klines += data
-        #start_date = datetime.fromtimestamp(data[-1][6] / 1000, timezone.utc) + timedelta(milliseconds=1)
-        start_date = datetime.utcfromtimestamp(data[-1][6] / 1000)
+    url = f'https://min-api.cryptocompare.com/data/v2/histohour?fsym={symbol}&tsym={tsym}&toTs={to_ts}&api_key={api_key}'
+    response = requests.get(url)
+    data = response.json()
     
-    df = pd.DataFrame(klines, columns=['timestamp', 'Open', 'High', 'Low', 'Close', 'Volume', 'Close_time', 'Quote_asset_volume', 'Number_of_trades', 'Taker_buy_base_asset_volume', 'Taker_buy_quote_asset_volume', 'Ignore'])
-    df['Close'] = df['Close'].astype(float)
-    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
-    df.set_index('timestamp', inplace=True)
-    print("completed function")
-    return df[['Close']]
+    if data['Response'] == 'Success':
+        data_points = data['Data']['Data']
+        df = pd.DataFrame(data_points)
+        df['time'] = pd.to_datetime(df['time'], unit='s')
+        df.set_index('time', inplace=True)
+        
+        # Filter data based on start_date
+        df = df[df.index >= start_date]
+        
+        return df[['close']]
+    else:
+        print(f"Error fetching data: {data['Message']}")
+        return None
 
 def calculate_ema(data, span=20):
     data['EMA'] = data['Close'].ewm(span=span, adjust=False).mean()