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Html (kind of) chart

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This commit is contained in:
Dobromir Popov 2025-02-04 21:49:35 +02:00
parent c9f7367bcf
commit fbff9c37a2
2 changed files with 125 additions and 33 deletions
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126
crypto/brian/index-deep-new.py
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@ -215,6 +215,92 @@ def load_best_checkpoint(model, best_dir=BEST_DIR):
model.load_state_dict(checkpoint["model_state_dict"]) model.load_state_dict(checkpoint["model_state_dict"])
return checkpoint return checkpoint
# --- Live HTML Chart Update ---
def update_live_html(candles, trade_history, epoch):
"""
Generate a chart image with buy/sell markers and a dotted line between open and close,
then embed it in a simple HTML page that auto-refreshes.
"""
from io import BytesIO
import base64
fig, ax = plt.subplots(figsize=(12, 6))
update_live_chart(ax, candles, trade_history)
ax.set_title(f"Live Trading Chart - Epoch {epoch}")
buf = BytesIO()
fig.savefig(buf, format='png')
plt.close(fig)
buf.seek(0)
image_base64 = base64.b64encode(buf.getvalue()).decode('utf-8')
html_content = f"""
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta http-equiv="refresh" content="10">
<title>Live Trading Chart - Epoch {epoch}</title>
<style>
body {{
margin: 0;
padding: 0;
display: flex;
justify-content: center;
align-items: center;
background-color: #f4f4f4;
}}
.chart-container {{
text-align: center;
}}
img {{
max-width: 100%;
height: auto;
}}
</style>
</head>
<body>
<div class="chart-container">
<h2>Live Trading Chart - Epoch {epoch}</h2>
<img src="data:image/png;base64,{image_base64}" alt="Live Chart"/>
</div>
</body>
</html>
"""
with open("live_chart.html", "w") as f:
f.write(html_content)
print("Updated live_chart.html.")
# --- Chart Drawing Helpers (used by both live preview and HTML update) ---
def update_live_chart(ax, candles, trade_history):
"""
Plot the chart with close price, buy and sell markers, and dotted lines joining buy/sell entry/exit.
"""
ax.clear()
close_prices = [candle["close"] for candle in candles]
x = list(range(len(close_prices)))
ax.plot(x, close_prices, label="Close Price", color="black", linewidth=1)
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"]
if not buy_label_added:
ax.plot(in_idx, in_price, marker="^", color="green", markersize=10, label="BUY")
buy_label_added = True
else:
ax.plot(in_idx, in_price, marker="^", color="green", markersize=10)
if not sell_label_added:
ax.plot(out_idx, out_price, marker="v", color="red", markersize=10, label="SELL")
sell_label_added = True
else:
ax.plot(out_idx, out_price, marker="v", color="red", markersize=10)
ax.plot([in_idx, out_idx], [in_price, out_price], linestyle="dotted", color="blue")
ax.set_xlabel("Candle Index")
ax.set_ylabel("Price")
ax.legend()
ax.grid(True)
# --- Backtest Environment --- # --- Backtest Environment ---
class BacktestEnvironment: class BacktestEnvironment:
def __init__(self, candles_dict, base_tf, timeframes): def __init__(self, candles_dict, base_tf, timeframes):
@ -284,7 +370,7 @@ class BacktestEnvironment:
return len(self.candles_dict[self.base_tf]) return len(self.candles_dict[self.base_tf])
# --- Enhanced Training Loop --- # --- Enhanced Training Loop ---
def train_on_historical_data(env, model, device, args, start_epoch, optimizer, scheduler): def train_on_historical_data(env, model, device, args, start_epoch, optimizer, scheduler, base_candles):
for epoch in range(start_epoch, args.epochs): for epoch in range(start_epoch, args.epochs):
state = env.reset() state = env.reset()
total_loss = 0 total_loss = 0
@ -309,40 +395,13 @@ def train_on_historical_data(env, model, device, args, start_epoch, optimizer, s
break break
state = next_state state = next_state
scheduler.step() scheduler.step()
epoch_loss = total_loss/len(env) epoch_loss = total_loss / len(env)
print(f"Epoch {epoch+1} Loss: {epoch_loss:.4f}") print(f"Epoch {epoch+1} Loss: {epoch_loss:.4f}")
save_checkpoint(model, optimizer, epoch, total_loss) save_checkpoint(model, optimizer, epoch, total_loss)
# Update the live HTML file with the current epoch chart
update_live_html(base_candles, env.trade_history, epoch+1)
# --- Live Plotting Functions --- # --- Live Plotting Functions (For live mode) ---
def update_live_chart(ax, candles, trade_history):
ax.clear()
close_prices = [candle["close"] for candle in candles]
x = list(range(len(close_prices)))
ax.plot(x, close_prices, label="Close Price", color="black", linewidth=1)
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"]
if not buy_label_added:
ax.plot(in_idx, in_price, marker="^", color="green", markersize=10, label="BUY")
buy_label_added = True
else:
ax.plot(in_idx, in_price, marker="^", color="green", markersize=10)
if not sell_label_added:
ax.plot(out_idx, out_price, marker="v", color="red", markersize=10, label="SELL")
sell_label_added = True
else:
ax.plot(out_idx, out_price, marker="v", color="red", markersize=10)
ax.plot([in_idx, out_idx], [in_price, out_price], linestyle="dotted", color="blue")
ax.set_title("Live Trading Chart")
ax.set_xlabel("Candle Index")
ax.set_ylabel("Price")
ax.legend()
ax.grid(True)
def live_preview_loop(candles, env): def live_preview_loop(candles, env):
plt.ion() plt.ion()
fig, ax = plt.subplots(figsize=(12, 6)) fig, ax = plt.subplots(figsize=(12, 6))
@ -406,7 +465,8 @@ async def main():
print("Loaded optimizer state from checkpoint.") print("Loaded optimizer state from checkpoint.")
else: else:
print("No valid optimizer state found in checkpoint; starting fresh optimizer state.") print("No valid optimizer state found in checkpoint; starting fresh optimizer state.")
train_on_historical_data(env, model, device, args, start_epoch, optimizer, scheduler) # Pass base candles from the base timeframe for HTML chart updates.
train_on_historical_data(env, model, device, args, start_epoch, optimizer, scheduler, candles_dict[base_tf])
elif args.mode == 'live': elif args.mode == 'live':
load_best_checkpoint(model) load_best_checkpoint(model)

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