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| import 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 | |
| import firebase_admin | |
| from firebase_admin import credentials, db | |
| import os | |
| cred = credentials.Certificate("credentials.json") | |
| firebase_admin.initialize_app(cred, {"databaseURL": "https://socail-swap-default-rtdb.asia-southeast1.firebasedatabase.app/"}) | |
| ref = db.reference() | |
| stopmodel = False | |
| buy_signals = [] | |
| sell_signals = [] | |
| class TradingEnv(gym.Env): | |
| def __init__(self, data, window_size=50): | |
| super(TradingEnv, self).__init__() | |
| self.data = data | |
| self.window_size = window_size | |
| self.current_step = window_size | |
| self.action_space = spaces.Discrete(3) | |
| self.observation_space = spaces.Box( | |
| low=0, high=1, shape=(window_size, 2), dtype=np.float32) | |
| def reset(self): | |
| self.current_step = self.window_size | |
| return self._get_observation() | |
| def _get_observation(self): | |
| window_data = self.data[self.current_step-self.window_size:self.current_step] | |
| obs = window_data[['Close', 'EMA']].values | |
| obs = (obs - obs.min()) / (obs.max() - obs.min()) | |
| return obs | |
| def step(self, action): | |
| reward = 0 | |
| done = False | |
| self.current_step += 1 | |
| if self.current_step >= len(self.data): | |
| done = True | |
| else: | |
| if action == 1: | |
| reward = self.data['Close'].iloc[self.current_step] - self.data['Close'].iloc[self.current_step - 1] | |
| elif action == 2: | |
| 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='ETHUSDT', interval='1h', start_date='2021-01-01'): | |
| end_date = datetime.utcnow() | |
| start_date = datetime.strptime(start_date, '%Y-%m-%d') | |
| klines = [] | |
| 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) | |
| data = response.json() | |
| if not data: | |
| break | |
| klines += data | |
| start_date = datetime.utcfromtimestamp(data[-1][6] / 1000) | |
| 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) | |
| return df[['Close']] | |
| 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 = [] | |
| 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]] | |
| 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] | |
| all_signals_data = buy_signals_data + sell_signals_data | |
| ref.child('signals').child('data').set(all_signals_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() |