fix checkpoint model loading
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Dobromir Popov
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commit
0ebf4e13bd
File diff suppressed because one or more lines are too long
@ -47,7 +47,7 @@ def save_candles_cache(filename, candles_dict):
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print("Error saving cache file:", e)
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# -------------------------------------
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# Checkpoint Functions (same as before)
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# Checkpoint Functions
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# -------------------------------------
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def maintain_checkpoint_directory(directory, max_files=10):
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files = os.listdir(directory)
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@ -100,6 +100,8 @@ def save_checkpoint(model, epoch, reward, last_dir=LAST_DIR, best_dir=BEST_DIR):
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print(f"Saved checkpoint for epoch {epoch} with reward {reward:.4f}")
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def load_best_checkpoint(model, best_dir=BEST_DIR):
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"""Attempt to load the best checkpoint. If the architecture is different,
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catch the RuntimeError and skip loading."""
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best_models = get_best_models(best_dir)
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if not best_models:
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return None
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@ -107,7 +109,13 @@ def load_best_checkpoint(model, best_dir=BEST_DIR):
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path = os.path.join(best_dir, best_file)
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print(f"Loading best model from checkpoint: {best_file} with reward {best_reward:.4f}")
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checkpoint = torch.load(path)
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model.load_state_dict(checkpoint["model_state_dict"])
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try:
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model.load_state_dict(checkpoint["model_state_dict"])
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except RuntimeError as e:
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print("Warning: Failed to load best checkpoint due to:")
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print(e)
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print("This is likely due to a change in model architecture. Skipping checkpoint load.")
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return None
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return checkpoint
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# -------------------------------------
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@ -283,7 +291,6 @@ class BacktestEnvironment:
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base_ts = base_candle["timestamp"]
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for tf in self.timeframes:
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candles_list = self.candles_dict[tf]
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# Get the candle from this timeframe that is closest to (and <=) base_ts.
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aligned_index, _ = get_aligned_candle_with_index(candles_list, base_ts)
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features = get_features_for_tf(candles_list, aligned_index, period=10)
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state_features.extend(features)
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@ -392,15 +399,15 @@ def train_on_historical_data(env, rl_agent, num_epochs=10, epsilon=0.1):
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# -------------------------------------
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async def main_backtest():
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symbol = 'BTC/USDT'
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# Define timeframes: we'll use 5 different ones.
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# Define timeframes: 5 different ones.
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timeframes = ["1m", "5m", "15m", "1h", "1d"]
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now = int(time.time() * 1000)
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# Use the base timeframe period of 1500 candles. For 1m, that is 1500 minutes.
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# For base timeframe 1m, get 1500 candles (1500 minutes)
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period_ms = 1500 * 60 * 1000
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since = now - period_ms
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end_time = now
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# Initialize exchange using MEXC (or your preferred exchange).
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# Initialize exchange using MEXC
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mexc_api_key = os.environ.get('MEXC_API_KEY', 'YOUR_API_KEY')
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mexc_api_secret = os.environ.get('MEXC_API_SECRET', 'YOUR_SECRET_KEY')
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exchange = ccxt.mexc({
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@ -409,58 +416,60 @@ async def main_backtest():
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'enableRateLimit': True,
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})
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candles_dict = {}
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for tf in timeframes:
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print(f"Fetching historical data for timeframe {tf}...")
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candles = await fetch_historical_data(exchange, symbol, tf, since, end_time, batch_size=500)
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candles_dict[tf] = candles
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try:
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candles_dict = {}
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for tf in timeframes:
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print(f"Fetching historical data for timeframe {tf}...")
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candles = await fetch_historical_data(exchange, symbol, tf, since, end_time, batch_size=500)
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candles_dict[tf] = candles
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# Optionally, save the multi-timeframe cache.
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save_candles_cache(CACHE_FILE, candles_dict)
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# Optionally, save the multi-timeframe cache.
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save_candles_cache(CACHE_FILE, candles_dict)
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# Create the backtest environment using multi-timeframe data.
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env = BacktestEnvironment(candles_dict, base_tf="1m", timeframes=timeframes)
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# Create the backtest environment using multi-timeframe data.
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env = BacktestEnvironment(candles_dict, base_tf="1m", timeframes=timeframes)
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# Neural Network dimensions: each timeframe produces 7 features.
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input_dim = len(timeframes) * 7 # 7 features * 5 timeframes = 35.
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hidden_dim = 128
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output_dim = 3 # Actions: SELL, HOLD, BUY.
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# Neural network dimensions: each timeframe produces 7 features.
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input_dim = len(timeframes) * 7 # 7 features x 5 timeframes = 35.
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hidden_dim = 128
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output_dim = 3 # Actions: SELL, HOLD, BUY.
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model = TradingModel(input_dim, hidden_dim, output_dim)
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optimizer = optim.Adam(model.parameters(), lr=1e-4)
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replay_buffer = ReplayBuffer(capacity=10000)
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rl_agent = ContinuousRLAgent(model, optimizer, replay_buffer, batch_size=32, gamma=0.99)
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model = TradingModel(input_dim, hidden_dim, output_dim)
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optimizer = optim.Adam(model.parameters(), lr=1e-4)
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replay_buffer = ReplayBuffer(capacity=10000)
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rl_agent = ContinuousRLAgent(model, optimizer, replay_buffer, batch_size=32, gamma=0.99)
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# Load best checkpoint if available.
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load_best_checkpoint(model, BEST_DIR)
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# Load best checkpoint if available. (In case of architecture change, it will be skipped.)
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load_best_checkpoint(model, BEST_DIR)
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# Train the agent over the historical period.
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num_epochs = 10 # Adjust as needed.
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train_on_historical_data(env, rl_agent, num_epochs=num_epochs, epsilon=0.1)
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# Train the agent over the historical period.
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num_epochs = 10 # Adjust as needed.
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train_on_historical_data(env, rl_agent, num_epochs=num_epochs, epsilon=0.1)
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# Run a final simulation (without exploration) to record trade history.
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state = env.reset(clear_trade_history=True)
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done = False
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cumulative_reward = 0.0
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while not done:
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action = rl_agent.act(state, epsilon=0.0)
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state, reward, next_state, done = env.step(action)
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cumulative_reward += reward
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state = next_state
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print("Final simulation cumulative profit:", cumulative_reward)
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# Run a final simulation (without exploration) to record trade history.
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state = env.reset(clear_trade_history=True)
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done = False
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cumulative_reward = 0.0
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while not done:
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action = rl_agent.act(state, epsilon=0.0)
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state, reward, next_state, done = env.step(action)
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cumulative_reward += reward
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state = next_state
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print("Final simulation cumulative profit:", cumulative_reward)
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# Evaluate trade performance.
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trades = env.trade_history
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num_trades = len(trades)
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num_wins = sum(1 for trade in trades if trade["pnl"] > 0)
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win_rate = (num_wins / num_trades * 100) if num_trades > 0 else 0.0
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total_profit = sum(trade["pnl"] for trade in trades)
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print(f"Total trades: {num_trades}, Wins: {num_wins}, Win rate: {win_rate:.2f}%, Total Profit: {total_profit:.4f}")
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# Evaluate trade performance.
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trades = env.trade_history
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num_trades = len(trades)
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num_wins = sum(1 for trade in trades if trade["pnl"] > 0)
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win_rate = (num_wins / num_trades * 100) if num_trades > 0 else 0.0
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total_profit = sum(trade["pnl"] for trade in trades)
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print(f"Total trades: {num_trades}, Wins: {num_wins}, Win rate: {win_rate:.2f}%, Total Profit: {total_profit:.4f}")
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# Plot chart with buy/sell markers on the base timeframe ("1m").
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plot_trade_history(candles_dict["1m"], trades)
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await exchange.close()
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# Plot chart with buy/sell markers on the base timeframe ("1m").
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plot_trade_history(candles_dict["1m"], trades)
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finally:
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# Ensure that exchange resources are released even if errors occur.
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await exchange.close()
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if __name__ == "__main__":
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load_dotenv()
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