diff --git a/crypto/brian/index-deep-new.py b/crypto/brian/index-deep-new.py
new file mode 100644
index 0000000..92fb52e
--- /dev/null
+++ b/crypto/brian/index-deep-new.py
@@ -0,0 +1,215 @@
+#!/usr/bin/env python3
+import sys
+import asyncio
+if sys.platform == 'win32':
+    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
+
+import os
+import time
+import json
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from collections import deque
+from datetime import datetime
+import matplotlib.pyplot as plt
+import ccxt.async_support as ccxt
+import argparse
+from torch.nn import TransformerEncoder, TransformerEncoderLayer
+import math
+
+
+
+from dotenv import load_dotenv
+load_dotenv()
+
+
+# --- New Constants ---
+NUM_TIMEFRAMES = 5  # Example: ["1m", "5m", "15m", "1h", "1d"]
+NUM_INDICATORS = 20 # Example: 20 technical indicators
+FEATURES_PER_CHANNEL = 7 # HLOC + SMA_close + SMA_volume
+
+# --- Positional Encoding Module ---
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model, dropout=0.1, max_len=5000):
+        super().__init__()
+        self.dropout = nn.Dropout(p=dropout)
+        position = torch.arange(max_len).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
+        pe = torch.zeros(max_len, 1, d_model)
+        pe[:, 0, 0::2] = torch.sin(position * div_term)
+        pe[:, 0, 1::2] = torch.cos(position * div_term)
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        x = x + self.pe[:x.size(0)]
+        return self.dropout(x)
+
+# --- Enhanced Transformer Model ---
+class TradingModel(nn.Module):
+    def __init__(self, num_channels, num_timeframes, hidden_dim=128):
+        super().__init__()
+        self.channel_branches = nn.ModuleList([
+            nn.Sequential(
+                nn.Linear(FEATURES_PER_CHANNEL, hidden_dim),
+                nn.LayerNorm(hidden_dim),
+                nn.GELU(),
+                nn.Dropout(0.1)
+            ) for _ in range(num_channels)
+        ])
+        
+        self.timeframe_embed = nn.Embedding(num_timeframes, hidden_dim)
+        self.pos_encoder = PositionalEncoding(hidden_dim)
+        
+        # Transformer
+        encoder_layers = TransformerEncoderLayer(
+            d_model=hidden_dim, nhead=4, dim_feedforward=512,
+            dropout=0.1, activation='gelu', batch_first=False
+        )
+        self.transformer = TransformerEncoder(encoder_layers, num_layers=2)
+        
+        # Attention Pooling
+        self.attn_pool = nn.Linear(hidden_dim, 1)
+        
+        # Prediction Heads
+        self.high_pred = nn.Sequential(
+            nn.Linear(hidden_dim, hidden_dim//2),
+            nn.GELU(),
+            nn.Linear(hidden_dim//2, 1)
+        )
+        self.low_pred = nn.Sequential(
+            nn.Linear(hidden_dim, hidden_dim//2),
+            nn.GELU(),
+            nn.Linear(hidden_dim//2, 1)
+        )
+
+    def forward(self, x, timeframe_ids):
+        # x shape: [batch_size, num_channels, features]
+        batch_size, num_channels, _ = x.shape
+        
+        # Process each channel
+        channel_outs = []
+        for i in range(num_channels):
+            channel_out = self.channel_branches[i](x[:,i,:])
+            channel_outs.append(channel_out)
+        
+        # Stack and add embeddings
+        stacked = torch.stack(channel_outs, dim=1) # [batch, channels, hidden]
+        stacked = stacked.permute(1, 0, 2) # [channels, batch, hidden]
+        
+        # Add timeframe embeddings
+        tf_embeds = self.timeframe_embed(timeframe_ids).unsqueeze(1)
+        stacked = stacked + tf_embeds
+        
+        # Transformer
+        src_mask = torch.triu(torch.ones(stacked.size(0), stacked.size(0)), diagonal=1).bool()
+        transformer_out = self.transformer(stacked, src_mask=src_mask.to(x.device))
+        
+        # Attention Pool
+        attn_weights = torch.softmax(self.attn_pool(transformer_out), dim=0)
+        aggregated = (transformer_out * attn_weights).sum(dim=0)
+        
+        return self.high_pred(aggregated).squeeze(), self.low_pred(aggregated).squeeze()
+
+# --- Enhanced Data Processing ---
+class BacktestEnvironment:
+    def get_state(self, index):
+        """Returns shape [num_channels, FEATURES_PER_CHANNEL]"""
+        state_features = []
+        base_ts = self.candles_dict[self.base_tf][index]["timestamp"]
+        
+        # Timeframe channels
+        for tf in self.timeframes:
+            aligned_idx, _ = get_aligned_candle_with_index(self.candles_dict[tf], base_ts)
+            features = get_features_for_tf(self.candles_dict[tf], aligned_idx)
+            state_features.append(features)
+            
+        # Indicator channels (placeholder - implement your indicators)
+        for _ in range(NUM_INDICATORS):
+            # Add indicator calculation here
+            state_features.append([0.0]*FEATURES_PER_CHANNEL)
+            
+        return np.array(state_features, dtype=np.float32)
+
+# --- Enhanced Training Loop ---
+def train_on_historical_data(env, model, device, args):
+    optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-5)
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs)
+    
+    for epoch in range(args.epochs):
+        state = env.reset()
+        total_loss = 0
+        model.train()
+        
+        while True:
+            # Prepare batch
+            state_tensor = torch.FloatTensor(state).unsqueeze(0).to(device)
+            timeframe_ids = torch.arange(state.shape[0]).to(device)
+            
+            # Forward pass
+            pred_high, pred_low = model(state_tensor, timeframe_ids)
+            
+            # Get targets from next candle
+            _, _, next_state, done, actual_high, actual_low = env.step(None)  # Dummy action
+            target_high = torch.FloatTensor([actual_high]).to(device)
+            target_low = torch.FloatTensor([actual_low]).to(device)
+            
+            # Custom loss
+            high_loss = torch.abs(pred_high - target_high) * 2
+            low_loss = torch.abs(pred_low - target_low) * 2
+            loss = (high_loss + low_loss).mean()
+            
+            # Backprop
+            optimizer.zero_grad()
+            loss.backward()
+            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+            optimizer.step()
+            
+            total_loss += loss.item()
+            
+            if done:
+                break
+            state = next_state
+        
+        scheduler.step()
+        print(f"Epoch {epoch+1} Loss: {total_loss/len(env):.4f}")
+        save_checkpoint(model, epoch, total_loss)
+
+# --- Mode Handling ---
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--mode', choices=['train', 'live', 'inference'], default='train')
+    parser.add_argument('--epochs', type=int, default=100)
+    parser.add_argument('--lr', type=float, default=3e-4)
+    parser.add_argument('--threshold', type=float, default=0.005)
+    return parser.parse_args()
+
+async def main():
+    args = parse_args()
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    
+    # Initialize model
+    model = TradingModel(
+        num_channels=NUM_TIMEFRAMES+NUM_INDICATORS,
+        num_timeframes=NUM_TIMEFRAMES
+    ).to(device)
+    
+    if args.mode == 'train':
+        # Initialize environment and train
+        env = BacktestEnvironment(...)
+        train_on_historical_data(env, model, device, args)
+    elif args.mode == 'live':
+        # Load model and connect to live data
+        load_best_checkpoint(model)
+        while True:
+            # Process live data
+            # Make predictions and execute trades
+            await asyncio.sleep(1)
+    elif args.mode == 'inference':
+        # Load model and run inference
+        load_best_checkpoint(model)
+        # Generate signals without training
+
+if __name__ == "__main__":
+    asyncio.run(main())
\ No newline at end of file
diff --git a/crypto/brian/index.py b/crypto/brian/index.py
new file mode 100644
index 0000000..38bc89e
--- /dev/null
+++ b/crypto/brian/index.py
@@ -0,0 +1,488 @@
+#!/usr/bin/env python3
+import sys
+import asyncio
+if sys.platform == 'win32':
+    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
+
+from dotenv import load_dotenv
+import os
+import time
+import json
+import ccxt.async_support as ccxt
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import numpy as np
+from collections import deque
+from datetime import datetime
+import matplotlib.pyplot as plt
+
+# --- Directories for saving models ---
+LAST_DIR = os.path.join("models", "last")
+BEST_DIR = os.path.join("models", "best")
+os.makedirs(LAST_DIR, exist_ok=True)
+os.makedirs(BEST_DIR, exist_ok=True)
+
+CACHE_FILE = "candles_cache.json"
+
+# -------------------------------------
+# Utility functions for caching candles to file
+# -------------------------------------
+def load_candles_cache(filename):
+    if os.path.exists(filename):
+        try:
+            with open(filename, "r") as f:
+                data = json.load(f)
+                print(f"Loaded cached data from {filename}.")
+                return data
+        except Exception as e:
+            print("Error reading cache file:", e)
+    return {}
+
+def save_candles_cache(filename, candles_dict):
+    try:
+        with open(filename, "w") as f:
+            json.dump(candles_dict, f)
+    except Exception as e:
+        print("Error saving cache file:", e)
+
+# -------------------------------------
+# Checkpoint Functions (same as before)
+# -------------------------------------
+def maintain_checkpoint_directory(directory, max_files=10):
+    files = os.listdir(directory)
+    if len(files) > max_files:
+        full_paths = [os.path.join(directory, f) for f in files]
+        full_paths.sort(key=lambda x: os.path.getmtime(x))
+        for f in full_paths[: len(files) - max_files]:
+            os.remove(f)
+
+def get_best_models(directory):
+    best_files = []
+    for file in os.listdir(directory):
+        parts = file.split("_")
+        try:
+            r = float(parts[1])
+            best_files.append((r, file))
+        except Exception:
+            continue
+    return best_files
+
+def save_checkpoint(model, epoch, reward, last_dir=LAST_DIR, best_dir=BEST_DIR):
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    last_filename = f"model_last_epoch_{epoch}_{timestamp}.pt"
+    last_path = os.path.join(last_dir, last_filename)
+    torch.save({
+         "epoch": epoch,
+         "reward": reward,
+         "model_state_dict": model.state_dict()
+    }, last_path)
+    maintain_checkpoint_directory(last_dir, max_files=10)
+
+    best_models = get_best_models(best_dir)
+    add_to_best = False
+    if len(best_models) < 10:
+        add_to_best = True
+    else:
+        min_reward, min_file = min(best_models, key=lambda x: x[0])
+        if reward > min_reward:
+            add_to_best = True
+            os.remove(os.path.join(best_dir, min_file))
+    if add_to_best:
+        best_filename = f"best_{reward:.4f}_epoch_{epoch}_{timestamp}.pt"
+        best_path = os.path.join(best_dir, best_filename)
+        torch.save({
+            "epoch": epoch,
+            "reward": reward,
+            "model_state_dict": model.state_dict()
+        }, best_path)
+        maintain_checkpoint_directory(best_dir, max_files=10)
+    print(f"Saved checkpoint for epoch {epoch} with reward {reward:.4f}")
+
+def load_best_checkpoint(model, best_dir=BEST_DIR):
+    best_models = get_best_models(best_dir)
+    if not best_models:
+        return None
+    best_reward, best_file = max(best_models, key=lambda x: x[0])
+    path = os.path.join(best_dir, best_file)
+    print(f"Loading best model from checkpoint: {best_file} with reward {best_reward:.4f}")
+    checkpoint = torch.load(path)
+    model.load_state_dict(checkpoint["model_state_dict"])
+    return checkpoint
+
+# -------------------------------------
+# Technical Indicator Helper Functions
+# -------------------------------------
+def compute_sma(candles_list, index, period=10):
+    start = max(0, index - period + 1)
+    values = [candle["close"] for candle in candles_list[start:index+1]]
+    return sum(values) / len(values) if values else 0.0
+
+def compute_sma_volume(candles_list, index, period=10):
+    start = max(0, index - period + 1)
+    values = [candle["volume"] for candle in candles_list[start:index+1]]
+    return sum(values) / len(values) if values else 0.0
+
+def get_aligned_candle_with_index(candles_list, target_ts):
+    """Find the candle in the list whose timestamp is the largest that is <= target_ts."""
+    best_idx = 0
+    for i, candle in enumerate(candles_list):
+        if candle["timestamp"] <= target_ts:
+            best_idx = i
+        else:
+            break
+    return best_idx, candles_list[best_idx]
+
+def get_features_for_tf(candles_list, index, period=10):
+    """Return a vector of 7 features: open, high, low, close, volume, sma_close, sma_volume."""
+    candle = candles_list[index]
+    f_open = candle["open"]
+    f_high = candle["high"]
+    f_low = candle["low"]
+    f_close = candle["close"]
+    f_volume = candle["volume"]
+    sma_close = compute_sma(candles_list, index, period)
+    sma_volume = compute_sma_volume(candles_list, index, period)
+    return [f_open, f_high, f_low, f_close, f_volume, sma_close, sma_volume]
+
+# -------------------------------------
+# Neural Network Architecture Definition
+# -------------------------------------
+class TradingModel(nn.Module):
+    def __init__(self, input_dim, hidden_dim, output_dim):
+        super(TradingModel, self).__init__()
+        self.net = nn.Sequential(
+            nn.Linear(input_dim, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, output_dim)
+        )
+    
+    def forward(self, x):
+        return self.net(x)
+
+# -------------------------------------
+# Replay Buffer for Experience Storage
+# -------------------------------------
+class ReplayBuffer:
+    def __init__(self, capacity=10000):
+        self.buffer = deque(maxlen=capacity)
+    
+    def add(self, experience):
+        self.buffer.append(experience)
+    
+    def sample(self, batch_size):
+        indices = np.random.choice(len(self.buffer), size=batch_size, replace=False)
+        return [self.buffer[i] for i in indices]
+    
+    def __len__(self):
+        return len(self.buffer)
+
+# -------------------------------------
+# RL Agent with Q-Learning and Epsilon-Greedy Exploration
+# -------------------------------------
+class ContinuousRLAgent:
+    def __init__(self, model, optimizer, replay_buffer, batch_size=32, gamma=0.99):
+        self.model = model
+        self.optimizer = optimizer
+        self.replay_buffer = replay_buffer
+        self.batch_size = batch_size
+        self.loss_fn = nn.MSELoss()
+        self.gamma = gamma
+
+    def act(self, state, epsilon=0.1):
+        if np.random.rand() < epsilon:
+            return np.random.randint(0, 3)
+        state_tensor = torch.from_numpy(np.array(state, dtype=np.float32)).unsqueeze(0)
+        with torch.no_grad():
+            output = self.model(state_tensor)
+        return torch.argmax(output, dim=1).item()
+
+    def train_step(self):
+        if len(self.replay_buffer) < self.batch_size:
+            return
+        
+        batch = self.replay_buffer.sample(self.batch_size)
+        states, actions, rewards, next_states, dones = zip(*batch)
+        states_tensor = torch.from_numpy(np.array(states, dtype=np.float32))
+        actions_tensor = torch.tensor(actions, dtype=torch.int64)
+        rewards_tensor = torch.from_numpy(np.array(rewards, dtype=np.float32)).unsqueeze(1)
+        next_states_tensor = torch.from_numpy(np.array(next_states, dtype=np.float32))
+        dones_tensor = torch.tensor(dones, dtype=torch.float32).unsqueeze(1)
+        
+        Q_values = self.model(states_tensor)
+        current_Q = Q_values.gather(1, actions_tensor.unsqueeze(1))
+        with torch.no_grad():
+            next_Q_values = self.model(next_states_tensor)
+            max_next_Q = next_Q_values.max(1)[0].unsqueeze(1)
+            target = rewards_tensor + self.gamma * max_next_Q * (1.0 - dones_tensor)
+        loss = self.loss_fn(current_Q, target)
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+
+# -------------------------------------
+# Historical Data Fetching Function (for a given timeframe)
+# -------------------------------------
+async def fetch_historical_data(exchange, symbol, timeframe, since, end_time, batch_size=500):
+    candles = []
+    since_ms = since
+    while True:
+        try:
+            batch = await exchange.fetch_ohlcv(symbol, timeframe=timeframe, since=since_ms, limit=batch_size)
+        except Exception as e:
+            print(f"Error fetching historical data for {timeframe}:", e)
+            break
+        if not batch:
+            break
+        for c in batch:
+            candle_dict = {
+                'timestamp': c[0],
+                'open': c[1],
+                'high': c[2],
+                'low': c[3],
+                'close': c[4],
+                'volume': c[5]
+            }
+            candles.append(candle_dict)
+        last_timestamp = batch[-1][0]
+        if last_timestamp >= end_time:
+            break
+        since_ms = last_timestamp + 1
+    print(f"Fetched {len(candles)} candles for timeframe {timeframe}.")
+    return candles
+
+# -------------------------------------
+# Backtest Environment with Multi-Timeframe State
+# -------------------------------------
+class BacktestEnvironment:
+    def __init__(self, candles_dict, base_tf="1m", timeframes=None):
+        self.candles_dict = candles_dict  # dict of timeframe: candles_list
+        self.base_tf = base_tf
+        if timeframes is None:
+            self.timeframes = [base_tf]  # fallback to single timeframe
+        else:
+            self.timeframes = timeframes
+        self.trade_history = []   # record of closed trades
+        self.current_index = 0    # index on base_tf candles
+        self.position = None      # active position record
+
+    def reset(self, clear_trade_history=True):
+        self.current_index = 0
+        self.position = None
+        if clear_trade_history:
+            self.trade_history = []
+        return self.get_state(self.current_index)
+
+    def get_state(self, index):
+        """Construct the state as the concatenated features of all timeframes.
+        For each timeframe, find the aligned candle for the base timeframe’s timestamp."""
+        state_features = []
+        base_candle = self.candles_dict[self.base_tf][index]
+        base_ts = base_candle["timestamp"]
+        for tf in self.timeframes:
+            candles_list = self.candles_dict[tf]
+            # Get the candle from this timeframe that is closest to (and <=) base_ts.
+            aligned_index, _ = get_aligned_candle_with_index(candles_list, base_ts)
+            features = get_features_for_tf(candles_list, aligned_index, period=10)
+            state_features.extend(features)
+        return np.array(state_features, dtype=np.float32)
+
+    def step(self, action):
+        """
+        Simulate a trading step based on the base timeframe.
+          - If not in a position and action is BUY (2), record entry at next candle's open.
+          - If in a position and action is SELL (0), record exit at next candle's open, computing PnL.
+        Returns: (current_state, reward, next_state, done)
+        """
+        base_candles = self.candles_dict[self.base_tf]
+        if self.current_index >= len(base_candles) - 1:
+            return self.get_state(self.current_index), 0.0, None, True
+
+        current_state = self.get_state(self.current_index)
+        next_index = self.current_index + 1
+        next_state = self.get_state(next_index)
+        current_candle = base_candles[self.current_index]
+        next_candle = base_candles[next_index]
+        reward = 0.0
+
+        # Action mapping: 0 -> SELL, 1 -> HOLD, 2 -> BUY.
+        if self.position is None:
+            if action == 2:  # BUY signal: enter position at next candle's open.
+                entry_price = next_candle["open"]
+                self.position = {"entry_price": entry_price, "entry_index": self.current_index}
+        else:
+            if action == 0:  # SELL signal: close position at next candle's open.
+                exit_price = next_candle["open"]
+                reward = exit_price - self.position["entry_price"]
+                trade = {
+                    "entry_index": self.position["entry_index"],
+                    "entry_price": self.position["entry_price"],
+                    "exit_index": next_index,
+                    "exit_price": exit_price,
+                    "pnl": reward
+                }
+                self.trade_history.append(trade)
+                self.position = None
+
+        self.current_index = next_index
+        done = (self.current_index >= len(base_candles) - 1)
+        return current_state, reward, next_state, done
+
+# -------------------------------------
+# Chart Plotting: Trade History & PnL
+# -------------------------------------
+def plot_trade_history(candles, trade_history):
+    close_prices = [candle["close"] for candle in candles]
+    x = list(range(len(close_prices)))
+    plt.figure(figsize=(12, 6))
+    plt.plot(x, close_prices, label="Close Price", color="black", linewidth=1)
+
+    # Use these flags so that the label "BUY" or "SELL" is only shown once in the legend.
+    buy_label_added = False
+    sell_label_added = False
+
+    for trade in trade_history:
+        in_idx = trade["entry_index"]
+        out_idx = trade["exit_index"]
+        in_price = trade["entry_price"]
+        out_price = trade["exit_price"]
+        pnl = trade["pnl"]
+
+        # Plot BUY marker ("IN")
+        if not buy_label_added:
+            plt.plot(in_idx, in_price, marker="^", color="green", markersize=10, label="BUY (IN)")
+            buy_label_added = True
+        else:
+            plt.plot(in_idx, in_price, marker="^", color="green", markersize=10)
+        plt.text(in_idx, in_price, " IN", color="green", fontsize=8, verticalalignment="bottom")
+
+        # Plot SELL marker ("OUT")
+        if not sell_label_added:
+            plt.plot(out_idx, out_price, marker="v", color="red", markersize=10, label="SELL (OUT)")
+            sell_label_added = True
+        else:
+            plt.plot(out_idx, out_price, marker="v", color="red", markersize=10)
+        plt.text(out_idx, out_price, " OUT", color="red", fontsize=8, verticalalignment="top")
+        
+        # Annotate the PnL near the SELL marker.
+        plt.text(out_idx, out_price, f" {pnl:+.2f}", color="blue", fontsize=8, verticalalignment="bottom")
+        
+        # Choose line color based on profitability.
+        if pnl > 0:
+            line_color = "green"
+        elif pnl < 0:
+            line_color = "red"
+        else:
+            line_color = "gray"
+        # Draw a dotted line between the buy and sell points.
+        plt.plot([in_idx, out_idx], [in_price, out_price], linestyle="dotted", color=line_color)
+
+    plt.title("Trade History with PnL")
+    plt.xlabel("Base Candle Index (1m)")
+    plt.ylabel("Price")
+    plt.legend()
+    plt.grid(True)
+    plt.show()
+
+# -------------------------------------
+# Training Loop: Backtesting Trading Episodes
+# -------------------------------------
+def train_on_historical_data(env, rl_agent, num_epochs=10, epsilon=0.1):
+    for epoch in range(1, num_epochs + 1):
+        state = env.reset()  # clear trade history each epoch
+        done = False
+        total_reward = 0.0
+        steps = 0
+        while not done:
+            action = rl_agent.act(state, epsilon=epsilon)
+            prev_state = state
+            state, reward, next_state, done = env.step(action)
+            if next_state is None:
+                next_state = np.zeros_like(prev_state)
+            rl_agent.replay_buffer.add((prev_state, action, reward, next_state, done))
+            rl_agent.train_step()
+            total_reward += reward
+            steps += 1
+        print(f"Epoch {epoch}/{num_epochs} completed, total reward: {total_reward:.4f} over {steps} steps.")
+        save_checkpoint(rl_agent.model, epoch, total_reward, LAST_DIR, BEST_DIR)
+
+# -------------------------------------
+# Main Asynchronous Function for Training & Charting
+# -------------------------------------
+async def main_backtest():
+    symbol = 'BTC/USDT'
+    # Define timeframes: we'll use 5 different ones.
+    timeframes = ["1m", "5m", "15m", "1h", "1d"]
+    now = int(time.time() * 1000)
+    # Use the base timeframe period of 1500 candles. For 1m, that is 1500 minutes.
+    period_ms = 1500 * 60 * 1000
+    since = now - period_ms
+    end_time = now
+
+    # Initialize exchange using MEXC (or your preferred exchange).
+    mexc_api_key = os.environ.get('MEXC_API_KEY', 'YOUR_API_KEY')
+    mexc_api_secret = os.environ.get('MEXC_API_SECRET', 'YOUR_SECRET_KEY')
+    exchange = ccxt.mexc({
+        'apiKey': mexc_api_key,
+        'secret': mexc_api_secret,
+        'enableRateLimit': True,
+    })
+
+    candles_dict = {}
+    for tf in timeframes:
+        print(f"Fetching historical data for timeframe {tf}...")
+        candles = await fetch_historical_data(exchange, symbol, tf, since, end_time, batch_size=500)
+        candles_dict[tf] = candles
+
+    # Optionally, save the multi-timeframe cache.
+    save_candles_cache(CACHE_FILE, candles_dict)
+
+    # Create the backtest environment using multi-timeframe data.
+    env = BacktestEnvironment(candles_dict, base_tf="1m", timeframes=timeframes)
+    
+    # Neural Network dimensions: each timeframe produces 7 features.
+    input_dim = len(timeframes) * 7  # 7 features * 5 timeframes = 35.
+    hidden_dim = 128
+    output_dim = 3  # Actions: SELL, HOLD, BUY.
+
+    model = TradingModel(input_dim, hidden_dim, output_dim)
+    optimizer = optim.Adam(model.parameters(), lr=1e-4)
+    replay_buffer = ReplayBuffer(capacity=10000)
+    rl_agent = ContinuousRLAgent(model, optimizer, replay_buffer, batch_size=32, gamma=0.99)
+
+    # Load best checkpoint if available.
+    load_best_checkpoint(model, BEST_DIR)
+
+    # Train the agent over the historical period.
+    num_epochs = 10  # Adjust as needed.
+    train_on_historical_data(env, rl_agent, num_epochs=num_epochs, epsilon=0.1)
+
+    # Run a final simulation (without exploration) to record trade history.
+    state = env.reset(clear_trade_history=True)
+    done = False
+    cumulative_reward = 0.0
+    while not done:
+        action = rl_agent.act(state, epsilon=0.0)
+        state, reward, next_state, done = env.step(action)
+        cumulative_reward += reward
+        state = next_state
+    print("Final simulation cumulative profit:", cumulative_reward)
+
+    # Evaluate trade performance.
+    trades = env.trade_history
+    num_trades = len(trades)
+    num_wins = sum(1 for trade in trades if trade["pnl"] > 0)
+    win_rate = (num_wins / num_trades * 100) if num_trades > 0 else 0.0
+    total_profit = sum(trade["pnl"] for trade in trades)
+    print(f"Total trades: {num_trades}, Wins: {num_wins}, Win rate: {win_rate:.2f}%, Total Profit: {total_profit:.4f}")
+
+    # Plot chart with buy/sell markers on the base timeframe ("1m").
+    plot_trade_history(candles_dict["1m"], trades)
+    
+    await exchange.close()
+
+if __name__ == "__main__":
+    load_dotenv()
+    asyncio.run(main_backtest())
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