wip
This commit is contained in:
Dobromir Popov
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@ -22,6 +22,25 @@ import matplotlib.dates as mdates
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from dotenv import load_dotenv
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load_dotenv()
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# --- Fetch Real 1s Data (if main_tf=="1s") ---
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def fetch_real_1s_data():
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"""
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Fetch real 1-second candle data from your API.
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Replace the URL and parameters with those required by your data provider.
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Expected data format: a list of dictionaries with keys: "timestamp", "open", "high", "low", "close", "volume"
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"""
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import requests
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url = "https://api.example.com/1s-data" # <-- Replace with your actual endpoint.
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try:
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response = requests.get(url)
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response.raise_for_status()
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data = response.json()
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print("Fetched real 1s data successfully.")
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return data
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except Exception as e:
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print("Failed to fetch real 1s data:", e)
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return []
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# --- Helper Function for Timestamp Conversion ---
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def convert_timestamp(ts):
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"""
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@ -309,7 +328,6 @@ def simulate_trades_1s(env):
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exit_idx = extrema[j+1]
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entry_price = env.candle_window[entry_idx]["open"]
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exit_price = env.candle_window[exit_idx]["open"]
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# If the entry candle’s close is lower than exit candle’s close, this is a long trade.
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if env.candle_window[entry_idx]["close"] < env.candle_window[exit_idx]["close"]:
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reward = exit_price - entry_price
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else:
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@ -330,8 +348,7 @@ def simulate_trades(model, env, device, args):
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Simulate trades over the current sliding window.
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If the main timeframe is 1s, use local extrema detection.
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Otherwise, check if the model's predicted potentials exceed the threshold.
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- If so, execute the model decision.
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- Otherwise, call the manual_trade override.
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If so, execute the model decision; otherwise, call the manual_trade override.
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"""
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if args.main_tf == "1s":
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simulate_trades_1s(env)
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@ -359,12 +376,13 @@ def simulate_trades(model, env, device, args):
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if env.current_index >= len(env.candle_window) - 1:
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break
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# --- Live HTML Chart Update (with Volume) ---
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def update_live_html(candles, trade_history, epoch):
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# --- Live HTML Chart Update (with Volume and Loss) ---
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def update_live_html(candles, trade_history, epoch, loss):
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"""
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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 chart displays price (line) and volume (bars on a secondary y-axis),
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and includes buy/sell markers with dotted connecting lines.
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The page title shows the epoch, loss, and PnL.
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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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@ -372,7 +390,7 @@ def update_live_html(candles, trade_history, epoch):
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fig, ax = plt.subplots(figsize=(12, 6))
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update_live_chart(ax, candles, trade_history)
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epoch_pnl = sum(trade["pnl"] for trade in trade_history)
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ax.set_title(f"Live Trading Chart - Epoch {epoch} | PnL: {epoch_pnl:.2f}")
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ax.set_title(f"Epoch {epoch} | Loss: {loss:.4f} | PnL: {epoch_pnl:.2f}")
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buf = BytesIO()
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fig.savefig(buf, format='png')
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plt.close(fig)
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@ -405,7 +423,7 @@ def update_live_html(candles, trade_history, epoch):
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</head>
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<body>
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<div class="chart-container">
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<h2>Live Trading Chart - Epoch {epoch} | PnL: {epoch_pnl:.2f}</h2>
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<h2>Epoch {epoch} | Loss: {loss:.4f} | PnL: {epoch_pnl:.2f}</h2>
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<img src="data:image/png;base64,{image_base64}" alt="Live Chart"/>
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</div>
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</body>
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@ -415,11 +433,12 @@ def update_live_html(candles, trade_history, epoch):
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f.write(html_content)
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print("Updated live_chart.html.")
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# --- Chart Drawing Helpers (with Volume) ---
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# --- Chart Drawing Helpers (with Volume and Date+Time) ---
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def update_live_chart(ax, candles, trade_history):
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"""
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Plot the price chart with actual timestamps and volume on a secondary y-axis.
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Mark BUY (green) and SELL (red) points and connect them with dotted lines.
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Plot the price chart with actual timestamps (date and time in short format)
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and volume on a secondary y-axis.
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Mark BUY (green) and SELL (red) points with dotted lines connecting.
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"""
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ax.clear()
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times = [convert_timestamp(candle["timestamp"]) for candle in candles]
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@ -427,7 +446,8 @@ def update_live_chart(ax, candles, trade_history):
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ax.plot(times, close_prices, label="Close Price", color="black", linewidth=1)
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ax.set_xlabel("Time")
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ax.set_ylabel("Price")
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ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))
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# Use short format for date and time.
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ax.xaxis.set_major_formatter(mdates.DateFormatter('%m-%d %H:%M'))
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# Plot volume on secondary y-axis.
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ax2 = ax.twinx()
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volumes = [candle["volume"] for candle in candles]
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@ -447,7 +467,7 @@ def update_live_chart(ax, candles, trade_history):
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ax.plot(entry_time, in_price, marker="^", color="green", markersize=10, label="BUY")
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ax.plot(exit_time, out_price, marker="v", color="red", markersize=10, label="SELL")
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ax.plot([entry_time, exit_time], [in_price, out_price], linestyle="dotted", color="blue")
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# Combine legends.
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# Combine legends from both axes.
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lines, labels = ax.get_legend_handles_labels()
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lines2, labels2 = ax2.get_legend_handles_labels()
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ax.legend(lines + lines2, labels + labels2)
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@ -455,7 +475,6 @@ def update_live_chart(ax, candles, trade_history):
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fig = ax.get_figure()
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fig.autofmt_xdate()
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# --- Backtest Environment with Sliding Window and Order Info ---
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class BacktestEnvironment:
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def __init__(self, candles_dict, base_tf, timeframes, window_size=None):
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@ -568,8 +587,7 @@ def train_on_historical_data(env, model, device, args, start_epoch, optimizer, s
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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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update_live_html(env.candle_window, env.trade_history, epoch+1, epoch_loss)
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# --- Live Plotting (for Live Mode) ---
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def live_preview_loop(candles, env):
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@ -607,6 +625,11 @@ async def main():
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print("Using device:", device)
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# Load cached candles.
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candles_dict = load_candles_cache(CACHE_FILE)
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# If the desired main timeframe is 1s, attempt to fetch real 1s data.
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if args.main_tf == "1s":
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real_1s_data = fetch_real_1s_data()
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if real_1s_data:
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candles_dict["1s"] = real_1s_data
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if not candles_dict:
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print("No historical candle data available for backtesting.")
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return
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