total PNL
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@ -365,7 +365,7 @@ def update_live_html(candles, trade_history, epoch):
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Generate an HTML page with a live chart.
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The chart displays price (line) and volume (bar chart on a secondary y-axis),
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and includes buy/sell markers with dotted lines connecting entries and exits.
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The page auto-refreshes every 10 seconds.
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The page auto-refreshes every 1 second.
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"""
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from io import BytesIO
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import base64
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@ -383,7 +383,7 @@ def update_live_html(candles, trade_history, epoch):
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<html>
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<head>
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<meta charset="utf-8">
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<meta http-equiv="refresh" content="10">
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<meta http-equiv="refresh" content="1">
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<title>Live Trading Chart - Epoch {epoch}</title>
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<style>
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body {{
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@ -535,6 +535,7 @@ class BacktestEnvironment:
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# --- Enhanced Training Loop ---
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def train_on_historical_data(env, model, device, args, start_epoch, optimizer, scheduler):
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lambda_trade = args.lambda_trade
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total_pnl = 0.0
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for epoch in range(start_epoch, args.epochs):
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env.reset()
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loss_accum = 0.0
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@ -563,10 +564,12 @@ def train_on_historical_data(env, model, device, args, start_epoch, optimizer, s
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loss_accum += loss.item()
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scheduler.step()
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epoch_loss = loss_accum / steps
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print(f"Epoch {epoch+1} Loss: {epoch_loss:.4f}")
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total_pnl += sum(trade["pnl"] for trade in env.trade_history)
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print(f"Epoch {epoch+1} Loss: {epoch_loss:.4f} | Total PnL: {total_pnl:.2f}")
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save_checkpoint(model, optimizer, epoch, loss_accum)
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simulate_trades(model, env, device, args)
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update_live_html(env.candle_window, env.trade_history, epoch+1)
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# --- Live Plotting (for Live Mode) ---
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def live_preview_loop(candles, env):
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