Update app.py

app.py

@@ -1,17 +1,18 @@
-import gym
+import gymnasium as gym
 import numpy as np
-import matplotlib.pyplot as plt
 import requests
 import pandas as pd
 from datetime import datetime, timedelta
 from stable_baselines3 import PPO
 from stable_baselines3.common.vec_env import DummyVecEnv
-from gym import spaces
-import time
+from gymnasium import spaces
 import firebase_admin
 from firebase_admin import credentials, db
 import os
+import threading
+import time
 
+# Firebase initialization
 cred = credentials.Certificate("credentials.json")
 firebase_admin.initialize_app(cred, {"databaseURL": "https://socail-swap-default-rtdb.asia-southeast1.firebasedatabase.app/"})
 ref = db.reference()
@@ -53,8 +54,8 @@ class TradingEnv(gym.Env):
                 reward = self.data['close'].iloc[self.current_step - 1] - self.data['close'].iloc[self.current_step]
 
         return self._get_observation(), reward, done, {}
-    
-def fetch_data(symbol='ETH', tsym='USD', start_date='2021-01-01', api_key='66bc686cb714fadda1fad0320704c98869d4b31ce7d9d27560c6c574b4d04c54'):
+
+def fetch_data(symbol='ETH', tsym='USD', start_date='2021-01-01', api_key='YOUR_API_KEY'):
     start_date = datetime.strptime(start_date, '%Y-%m-%d')
     end_date = datetime.utcnow()
     to_ts = int(end_date.timestamp())
@@ -68,10 +69,7 @@ def fetch_data(symbol='ETH', tsym='USD', start_date='2021-01-01', api_key='66bc6
         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']}")
@@ -81,68 +79,63 @@ def calculate_ema(data, span=20):
     data['EMA'] = data['close'].ewm(span=span, adjust=False).mean()
     return data
 
-def run_model():
-    while True:
-        try:
-            new_data = fetch_data(start_date=(datetime.utcnow() - timedelta(days=3)).strftime('%Y-%m-%d'))
-            new_data = calculate_ema(new_data)
-            
-            if len(new_data) < 50:
-                print("Not enough data to update the environment.")
-                time.sleep(3600)
-                continue
-            
-            env = DummyVecEnv([lambda: TradingEnv(new_data)])
-            model.set_env(env)
-            
-            obs = env.reset()
-            dates = new_data.index[50:]
-            prices = new_data['close'][50:]
-            emas = new_data['EMA'][50:]
-            actions = []
+def load_or_train_model(env):
+    if os.path.exists("./ppo_trading_agent.zip"):
+        model = PPO.load("ppo_trading_agent", env=env)
+    else:
+        model = PPO('MlpPolicy', env, verbose=1)
+        model.learn(total_timesteps=10000)
+        model.save("ppo_trading_agent")
+    return model
+
+def run_model(model, env, data):
+    obs = env.reset()
+    dates = data.index[50:]
+    prices = data['close'][50:]
+    emas = data['EMA'][50:]
+    actions = []
 
-            for date, price, ema in zip(dates, prices, emas):
-                action, _ = model.predict(obs)
-                actions.append(action[0])
-                obs, _, done, _ = env.step(action)
-                if done:
-                    break
+    for date, price, ema in zip(dates, prices, emas):
+        action, _ = model.predict(obs)
+        actions.append(action[0])
+        obs, _, done, _ = env.step(action)
+        if done:
+            break
 
-            new_buy_signals = [(date, price, ema) for date, price, ema, action in zip(dates, prices, emas, actions) if action == 1 and date not in [signal[0] for signal in buy_signals]]
-            new_sell_signals = [(date, price, ema) for date, price, ema, action in zip(dates, prices, emas, actions) if action == 2 and date not in [signal[0] for signal in sell_signals]]
+    new_buy_signals = [(date, price, ema) for date, price, ema, action in zip(dates, prices, emas, actions) if action == 1]
+    new_sell_signals = [(date, price, ema) for date, price, ema, action in zip(dates, prices, emas, actions) if action == 2]
 
-            for signal in new_buy_signals:
-                if signal[0] not in [s[0] for s in buy_signals] and signal[0] not in [s[0] for s in sell_signals]:
-                    buy_signals.append(signal)
-                
+    for signal in new_buy_signals:
+        if signal[0] not in [s[0] for s in buy_signals] and signal[0] not in [s[0] for s in sell_signals]:
+            buy_signals.append(signal)
+    
+    for signal in new_sell_signals:
+        if signal[0] not in [s[0] for s in sell_signals] and signal[0] not in [s[0] for s in buy_signals]:
+            sell_signals.append(signal)
+    
+    buy_signals_data = [{'timestamp': signal[0].strftime('%Y-%m-%d %H:%M:%S'), 'type': 'b', 'price': round(signal[1], 2), 'ema': round(signal[2], 2)} for signal in buy_signals]
+    sell_signals_data = [{'timestamp': signal[0].strftime('%Y-%m-%d %H:%M:%S'), 'type': 's', 'price': round(signal[1], 2), 'ema': round(signal[2], 2)} for signal in sell_signals]
 
-            for signal in new_sell_signals:
-                if signal[0] not in [s[0] for s in sell_signals] and signal[0] not in [s[0] for s in buy_signals]:
-                    sell_signals.append(signal)
-                    
-            buy_signals_data = [{'timestamp': signal[0].strftime('%Y-%m-%d %H:%M:%S'), 'type': 'b', 'price': round(signal[1], 2), 'ema': round(signal[2],2)} for signal in buy_signals]
-            sell_signals_data = [{'timestamp': signal[0].strftime('%Y-%m-%d %H:%M:%S'), 'type': 's', 'price': round(signal[1], 2), 'ema': round(signal[2],2)} for signal in sell_signals]
+    all_signals_data = buy_signals_data + sell_signals_data
 
-            all_signals_data = buy_signals_data + sell_signals_data
+    ref.child('signals').child('data').set(all_signals_data)
 
-            ref.child('signals').child('data').set(all_signals_data)
-            time.sleep(3600) 
+def background_task():
+    while True:
+        try:
+            new_data = fetch_data(start_date=(datetime.utcnow() - timedelta(days=3)).strftime('%Y-%m-%d'))
+            if new_data is not None:
+                new_data = calculate_ema(new_data)
+                if len(new_data) >= 50:
+                    env = DummyVecEnv([lambda: TradingEnv(new_data)])
+                    model = load_or_train_model(env)
+                    run_model(model, env, new_data)
+            time.sleep(3600)
         except Exception as e:
             print(f"An error occurred: {e}")
             break
-        
-if __name__ == "__main__":
-    data = fetch_data()
-    data = calculate_ema(data)
-    if len(data) < 50:
-        raise ValueError("Not enough data to fill the window size.")
 
-    env = DummyVecEnv([lambda: TradingEnv(data)])
-
-    if os.path.exists("./ppo_trading_agent.zip"):
-        model = PPO.load("ppo_trading_agent", env=env)
-    else:
-        model = PPO('MlpPolicy', env, verbose=1)
-        model.learn(total_timesteps=10000)
-    print("Ender function")
-    run_model()
+if __name__ == "__main__":
+    background_thread = threading.Thread(target=background_task)
+    background_thread.start()
+    background_thread.join()