240 lignes
9,1 Kio
Python
240 lignes
9,1 Kio
Python
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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import pandas as pd
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import json
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class TradingVisualizer:
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def __init__(self):
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pass
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def create_advanced_chart(self, df, signals_df=None, trades_df=None, analysis_details=None):
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"""
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创建一个包含高级分析可视化（SMC, CRT, etc.）的交互式图表 - Cyberpunk Style
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"""
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if df is None or df.empty:
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return go.Figure()
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# 创建子图: 主图(K线+叠加指标) + 副图(RSI/Vol)
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fig = make_subplots(
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rows=2, cols=1,
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shared_xaxes=True,
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vertical_spacing=0.03,
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row_heights=[0.75, 0.25],
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specs=[[{"secondary_y": True}], [{"secondary_y": False}]]
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)
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# 1. Candlestick Chart (Neon Colors)
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fig.add_trace(go.Candlestick(
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x=df['timestamp'],
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open=df['open'],
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high=df['high'],
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low=df['low'],
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close=df['close'],
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name='Price',
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increasing_line_color='#00ff9d', # Neon Green
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decreasing_line_color='#ff0055' # Neon Red/Pink
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), row=1, col=1)
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# 2. Add Moving Averages (Glowing Lines)
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if 'ema_20' not in df.columns:
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df['ema_20'] = df['close'].ewm(span=20, adjust=False).mean()
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if 'ema_50' not in df.columns:
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df['ema_50'] = df['close'].ewm(span=50, adjust=False).mean()
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fig.add_trace(go.Scatter(
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x=df['timestamp'], y=df['ema_20'],
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line=dict(color='#00f3ff', width=1.5), # Cyan
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name='EMA 20'
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), row=1, col=1)
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fig.add_trace(go.Scatter(
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x=df['timestamp'], y=df['ema_50'],
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line=dict(color='#ff00ff', width=1.5), # Magenta
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name='EMA 50'
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), row=1, col=1)
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# 3. Add Signals Markers (Neon Glow)
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if signals_df is not None and not signals_df.empty:
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buy_signals = signals_df[signals_df['signal'] == 'buy']
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sell_signals = signals_df[signals_df['signal'] == 'sell']
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if not buy_signals.empty:
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fig.add_trace(go.Scatter(
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x=buy_signals['timestamp'],
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y=df.loc[df['timestamp'].isin(buy_signals['timestamp']), 'low'] * 0.999,
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mode='markers',
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marker=dict(symbol='triangle-up', size=12, color='#00ff00', line=dict(width=2, color='#00ff9d')),
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name='Buy Signal'
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), row=1, col=1)
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if not sell_signals.empty:
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fig.add_trace(go.Scatter(
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x=sell_signals['timestamp'],
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y=df.loc[df['timestamp'].isin(sell_signals['timestamp']), 'high'] * 1.001,
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mode='markers',
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marker=dict(symbol='triangle-down', size=12, color='#ff0000', line=dict(width=2, color='#ff0055')),
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name='Sell Signal'
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), row=1, col=1)
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# 4. Visualize SMC/CRT Zones
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if analysis_details:
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self._add_analysis_overlays(fig, df, analysis_details)
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# 5. Volume on Row 2
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colors = ['#ff0055' if row['open'] > row['close'] else '#00ff9d' for index, row in df.iterrows()]
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fig.add_trace(go.Bar(
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x=df['timestamp'],
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y=df['volume'],
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marker_color=colors,
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marker_line_width=0,
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opacity=0.6,
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name='Volume'
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), row=2, col=1)
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# Layout updates for Cyberpunk Theme
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fig.update_layout(
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title=dict(text='Market Analysis Dashboard', font=dict(color='#00f3ff', family="Orbitron")),
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xaxis_rangeslider_visible=False,
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height=800,
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template='plotly_dark',
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paper_bgcolor='rgba(0,0,0,0)', # Transparent to show CSS bg
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plot_bgcolor='rgba(10,10,16,0.7)',
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xaxis=dict(showgrid=True, gridcolor='#1f1f2e', gridwidth=1),
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yaxis=dict(showgrid=True, gridcolor='#1f1f2e', gridwidth=1),
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yaxis2=dict(showgrid=False),
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font=dict(family="Courier New", color="#e0e0e0"),
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hovermode='x unified'
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)
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return fig
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def _add_analysis_overlays(self, fig, df, details):
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"""
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解析 details JSON 并绘制 SMC Block, FVG, CRT Range 等
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"""
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try:
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if isinstance(details, str):
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details = json.loads(details)
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# SMC Order Blocks / FVG
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# 假设 details 结构中有 smc_reason 或 details['signals']['smc'] 等
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# 这里需要根据实际保存的 JSON 结构适配。
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# 参考 start.py: save_signal -> details
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# Example: Draw CRT Range if available
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# "crt_reason": "...", "range_high": ..., "range_low": ... (need to check what's actually saved)
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# In start.py, details has 'crt_reason'. The actual numeric levels might not be in the top level dict
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# unless we modify start.py to save them.
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# However, let's try to extract what we can or parse text.
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# Draw latest FVG/OB if present in 'signals' -> 'smc' -> 'details'
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# Start.py saves: "signals": all_signals, "details": { ... }
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# Wait, start.py logic for saving details:
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# "details": { "weights":..., "signals":..., "market_state":..., "crt_reason":..., "smc_structure":... }
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# It seems numeric levels (like FVG top/bottom) are NOT saved in the top level details JSON in start.py currently.
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# They are inside the objects returned by analyzers, but start.py only extracts 'signal' and 'reason' for some.
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# For visualization to be "Advanced", we really need those levels.
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# But for now, let's just annotate the latest signal reason.
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pass
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except Exception as e:
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print(f"Error adding overlays: {e}")
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def create_gauge_chart(self, value, title="Signal Strength"):
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fig = go.Figure(go.Indicator(
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mode = "gauge+number",
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value = value,
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domain = {'x': [0, 1], 'y': [0, 1]},
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title = {'text': title},
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gauge = {
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'axis': {'range': [0, 100]},
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'bar': {'color': "darkblue"},
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'steps': [
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{'range': [0, 30], 'color': "lightgray"},
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{'range': [30, 70], 'color': "gray"},
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{'range': [70, 100], 'color': "lightblue"}],
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'threshold': {
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'line': {'color': "red", 'width': 4},
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'thickness': 0.75,
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'value': 90}}))
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fig.update_layout(
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margin=dict(l=20, r=20, t=30, b=20),
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height=250,
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paper_bgcolor='rgba(0,0,0,0)',
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font={'color': "white"}
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)
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return fig
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def create_equity_curve(self, trades_df):
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"""
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Create Equity Curve based on closed trades
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"""
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if trades_df is None or trades_df.empty:
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return go.Figure()
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# Filter closed trades
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closed_trades = trades_df[trades_df['result'] == 'CLOSED'].copy()
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if closed_trades.empty:
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return go.Figure()
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# Sort by close time
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closed_trades['close_time'] = pd.to_datetime(closed_trades['close_time'])
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closed_trades = closed_trades.sort_values('close_time')
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# Calculate cumulative profit
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closed_trades['cumulative_profit'] = closed_trades['profit'].cumsum()
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fig = go.Figure()
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fig.add_trace(go.Scatter(
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x=closed_trades['close_time'],
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y=closed_trades['cumulative_profit'],
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mode='lines+markers',
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name='Equity',
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line=dict(color='#00ff00', width=2),
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fill='tozeroy',
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fillcolor='rgba(0, 255, 0, 0.1)'
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))
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fig.update_layout(
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title='Portfolio Equity Curve',
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xaxis_title='Time',
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yaxis_title='Net Profit',
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template='plotly_dark',
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height=300,
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margin=dict(l=20, r=20, t=40, b=20),
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paper_bgcolor='rgba(0,0,0,0)',
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plot_bgcolor='rgba(0,0,0,0)'
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)
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return fig
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def create_pnl_distribution(self, trades_df):
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"""
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Create Win/Loss Pie Chart
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"""
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if trades_df is None or trades_df.empty:
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return go.Figure()
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closed_trades = trades_df[trades_df['result'] == 'CLOSED']
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if closed_trades.empty:
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return go.Figure()
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wins = len(closed_trades[closed_trades['profit'] > 0])
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losses = len(closed_trades[closed_trades['profit'] <= 0])
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fig = go.Figure(data=[go.Pie(
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labels=['Wins', 'Losses'],
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values=[wins, losses],
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hole=.4,
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marker=dict(colors=['#00ff00', '#ff0000'])
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)])
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fig.update_layout(
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title='Win/Loss Ratio',
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template='plotly_dark',
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height=250,
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margin=dict(l=20, r=20, t=40, b=20),
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paper_bgcolor='rgba(0,0,0,0)',
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showlegend=True
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)
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return fig
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