addychai355-create
420 lines
14 KiB
Python
420 lines
14 KiB
Python
"""
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Forex Quant Dashboard — Streamlit App
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Monitor signals, performance, and live prices from anywhere.
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Deploy to Streamlit Community Cloud for free:
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1. Push this folder to GitHub
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2. Go to https://streamlit.io/cloud
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3. Connect repo → Deploy
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"""
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import sys
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from pathlib import Path
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# Add project root to path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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import warnings
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warnings.filterwarnings("ignore")
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import streamlit as st
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import pandas as pd
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import numpy as np
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import plotly.graph_objects as go
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import plotly.express as px
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from plotly.subplots import make_subplots
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from datetime import datetime, timedelta, timezone
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from data.fx_data import get_forex_data, AVAILABLE_PAIRS
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from strategies.momentum import add_indicators, generate_signals, calculate_performance
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st.set_page_config(
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page_title="Forex Quant Monitor",
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page_icon="📊",
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layout="wide",
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initial_sidebar_state="expanded",
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)
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# ─── Color scheme ───
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COLORS = {
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"bg": "#0E1117",
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"card": "#1A1D23",
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"green": "#00C853",
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"red": "#FF1744",
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"blue": "#448AFF",
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"yellow": "#FFD600",
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"text": "#E0E0E0",
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}
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st.markdown("""
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<style>
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.stApp { background-color: #0E1117; }
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.css-1r6slb0 { background-color: #1A1D23; }
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.metric-card {
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background: #1A1D23;
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padding: 1rem;
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border-radius: 8px;
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border: 1px solid #2D3039;
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}
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.metric-value { font-size: 1.8rem; font-weight: 700; }
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.metric-label { font-size: 0.8rem; color: #9E9E9E; }
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.positive { color: #00C853; }
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.negative { color: #FF1744; }
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</style>
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""", unsafe_allow_html=True)
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# ─── Sidebar ───
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st.sidebar.title("📊 Forex Monitor")
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st.sidebar.markdown("---")
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# Pair selector
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pair = st.sidebar.selectbox("Pair", AVAILABLE_PAIRS, index=0)
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# Timeframe
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tf_options = {"1m": "1 Min", "5m": "5 Min", "15m": "15 Min", "30m": "30 Min",
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"1h": "1 Hour", "4h": "4 Hour", "1d": "1 Day"}
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tf = st.sidebar.selectbox("Timeframe", list(tf_options.keys()),
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format_func=lambda x: tf_options[x], index=4)
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# Date range
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years_back = st.sidebar.slider("History", 1, 5, 2)
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st.sidebar.markdown("---")
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st.sidebar.subheader("Strategy Params")
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atr_min = st.sidebar.slider("Min ATR %", 0.01, 0.50, 0.05, 0.01)
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use_macd = st.sidebar.checkbox("MACD Filter", value=True)
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st.sidebar.markdown("---")
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st.sidebar.caption("Data: Yahoo Finance (free)")
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st.sidebar.caption(f"Updated: {datetime.now(timezone.utc):%Y-%m-%d %H:%M} UTC")
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auto_refresh = st.sidebar.checkbox("Auto-refresh every 60s", value=False)
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if auto_refresh:
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st.sidebar.info("🔄 Refreshing...")
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st.rerun(60)
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# ─── Load Data ───
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@st.cache_data(ttl=300) # 5 min cache
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def load_data(pr, tf_str, yrs):
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"""Load forex data with caching."""
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df = get_forex_data(pr, tf_str, years_back=yrs, cache=True, source="yahoo")
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if df.empty or len(df) < 50:
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return None
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df = add_indicators(df)
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df = generate_signals(df, atr_min_pct=atr_min, use_macd_filter=use_macd)
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return df
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@st.cache_data(ttl=300)
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def load_all_pairs_data(tf_str, yrs):
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"""Load latest data for all pairs (for overview)."""
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results = {}
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for p in AVAILABLE_PAIRS:
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try:
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df = get_forex_data(p, tf_str, years_back=yrs, cache=True, source="yahoo")
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if not df.empty and len(df) > 20:
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results[p] = df
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except Exception:
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continue
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return results
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# ─── Main Dashboard ───
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# Row 1: Live Prices Overview
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st.subheader("💰 Live Prices Overview")
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with st.spinner("Loading market data..."):
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all_data = load_all_pairs_data("1h", 1)
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if all_data:
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cols = st.columns(4)
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for i, (p, df) in enumerate(sorted(all_data.items())):
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latest = df.iloc[-1]
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prev = df.iloc[-2]
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change = latest["close"] - prev["close"]
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change_pct = change / prev["close"] * 100
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with cols[i % 4]:
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color = COLORS["green"] if change >= 0 else COLORS["red"]
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arrow = "▲" if change >= 0 else "▼"
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st.markdown(f"""
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<div class="metric-card">
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<div class="metric-label">{p.replace('_', '/')}</div>
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<div class="metric-value">{latest['close']:.5f}</div>
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<div style="color:{color}">
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{arrow} {change:.5f} ({change_pct:+.3f}%)
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</div>
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</div>
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""", unsafe_allow_html=True)
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else:
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st.warning("Could not load price data. Check internet connection.")
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st.markdown("---")
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# Row 2: Main Strategy Chart
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st.subheader(f"📈 {pair.replace('_', '/')} — Strategy Analysis")
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data = load_data(pair, tf, years_back)
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if data is not None:
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col1, col2 = st.columns([2, 1])
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with col1:
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# Price + signals chart
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fig = make_subplots(
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rows=3, cols=1,
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shared_xaxes=True,
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vertical_spacing=0.05,
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row_heights=[0.55, 0.25, 0.20],
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subplot_titles=(f"{pair.replace('_', '/')} Price & Signals", "MACD", "RSI"),
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)
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# Candlestick chart
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fig.add_trace(go.Candlestick(
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x=data["time"],
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open=data["open"],
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high=data["high"],
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low=data["low"],
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close=data["close"],
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name="Price",
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showlegend=False,
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), row=1, col=1)
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# Buy/Sell markers
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buy_signals = data[data["signal"] == 1]
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fig.add_trace(go.Scatter(
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x=buy_signals["time"],
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y=buy_signals["close"],
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mode="markers",
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marker=dict(symbol="triangle-up", size=12, color=COLORS["green"]),
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name="Enter Long",
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), row=1, col=1)
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# MAs
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fig.add_trace(go.Scatter(
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x=data["time"], y=data["ma_fast"],
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line=dict(color=COLORS["blue"], width=1),
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name="MA-8",
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), row=1, col=1)
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fig.add_trace(go.Scatter(
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x=data["time"], y=data["ma_mid"],
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line=dict(color=COLORS["yellow"], width=1),
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name="MA-21",
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), row=1, col=1)
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# MACD
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fig.add_trace(go.Bar(
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x=data["time"], y=data["macd_hist"],
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marker_color=np.where(data["macd_hist"] >= 0, COLORS["green"], COLORS["red"]),
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name="MACD Hist",
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), row=2, col=1)
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fig.add_trace(go.Scatter(
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x=data["time"], y=data["macd"],
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line=dict(color=COLORS["blue"], width=1.5),
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name="MACD",
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), row=2, col=1)
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fig.add_trace(go.Scatter(
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x=data["time"], y=data["macd_signal"],
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line=dict(color=COLORS["yellow"], width=1.5),
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name="Signal",
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), row=2, col=1)
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# RSI
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fig.add_trace(go.Scatter(
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x=data["time"], y=data["rsi"],
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line=dict(color=COLORS["blue"], width=1.5),
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name="RSI",
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), row=3, col=1)
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fig.add_hline(y=70, line_dash="dash", line_color=COLORS["red"], row=3, col=1)
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fig.add_hline(y=30, line_dash="dash", line_color=COLORS["green"], row=3, col=1)
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fig.update_layout(
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height=650,
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template="plotly_dark",
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hovermode="x unified",
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margin=dict(l=0, r=0, t=30, b=0),
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legend=dict(orientation="h", y=1.02, x=0),
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)
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fig.update_xaxes(rangeslider_visible=False)
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st.plotly_chart(fig, use_container_width=True)
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with col2:
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# Strategy metrics
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perf = calculate_performance(data)
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st.markdown("### 📊 Performance")
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metrics = [
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("Return", f"{perf['total_return_pct']:+.2f}%", "positive" if perf['total_return_pct'] > 0 else "negative"),
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("Buy & Hold", f"{perf['buy_hold_return_pct']:+.2f}%", "positive" if perf['buy_hold_return_pct'] > 0 else "negative"),
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("Sharpe", f"{perf['sharpe_ratio']}", "positive" if perf['sharpe_ratio'] > 1 else "neutral" if perf['sharpe_ratio'] > 0 else "negative"),
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("Max Drawdown", f"{perf['max_drawdown_pct']:.2f}%", "negative"),
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("Win Rate", f"{perf['win_rate_pct']:.1f}%", "positive" if perf['win_rate_pct'] > 50 else "negative"),
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("Trades", f"{perf['num_trades']}", "neutral"),
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("Exposure", f"{perf['exposure_pct']:.1f}%", "neutral"),
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]
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for label, value, cls in metrics:
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st.markdown(f"""
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<div style="display:flex; justify-content:space-between; padding:4px 0; border-bottom:1px solid #2D3039;">
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<span style="color:#9E9E9E;">{label}</span>
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<span class="{cls}" style="font-weight:600;">{value}</span>
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</div>
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""", unsafe_allow_html=True)
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st.markdown("---")
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# Current signal
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latest_signal = data["signal"].iloc[-1]
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latest_position = data["position"].iloc[-1]
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latest_rsi = data["rsi"].iloc[-1]
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latest_atr = data["atr_pct"].iloc[-1]
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st.markdown("### 🔔 Current Status")
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signal_icon = "🟢" if latest_position == 1 else "🔴" if latest_position == -1 else "⚪"
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signal_text = "LONG" if latest_position == 1 else "SHORT" if latest_position == -1 else "FLAT"
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st.markdown(f"""
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<div class="metric-card" style="text-align:center;">
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<div style="font-size:2rem;">{signal_icon}</div>
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<div style="font-size:1.5rem; font-weight:700;">{signal_text}</div>
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<div style="color:#9E9E9E;">RSI: {latest_rsi:.1f} | ATR%: {latest_atr:.3f}%</div>
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</div>
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""", unsafe_allow_html=True)
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else:
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st.error(f"Could not load data for {pair}. Try a different pair or timeframe.")
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st.markdown("---")
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# Row 3: Equity Curve + Drawdown
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st.subheader("💰 Equity Curve")
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if data is not None:
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col1, col2 = st.columns([2, 1])
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with col1:
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# Compute equity curve from signals
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df = data.copy()
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df["returns"] = df["close"].pct_change()
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df["strategy_returns"] = df["position"].shift(1) * df["returns"]
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df["trades"] = df["position"].diff().abs().clip(0)
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df["strategy_returns"] -= df["trades"] * 0.0001 / df["close"]
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df["equity"] = 10000 * (1 + df["strategy_returns"]).cumprod()
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df["buy_hold"] = 10000 * (1 + df["returns"]).cumprod()
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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=0.05,
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row_heights=[0.7, 0.3],
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)
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fig.add_trace(go.Scatter(
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x=df["time"], y=df["equity"],
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line=dict(color=COLORS["green"], width=2),
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name="Strategy",
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), row=1, col=1)
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fig.add_trace(go.Scatter(
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x=df["time"], y=df["buy_hold"],
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line=dict(color="#9E9E9E", width=1, dash="dash"),
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name="Buy & Hold",
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), row=1, col=1)
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# Drawdown
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peak = df["equity"].expanding().max()
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dd = (df["equity"] - peak) / peak * 100
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fig.add_trace(go.Scatter(
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x=df["time"], y=dd,
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fill="tozeroy",
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line=dict(color=COLORS["red"], width=1),
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name="Drawdown %",
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), row=2, col=1)
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fig.update_layout(
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height=400,
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template="plotly_dark",
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hovermode="x unified",
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margin=dict(l=0, r=0, t=10, b=0),
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legend=dict(orientation="h", y=1.02, x=0),
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)
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st.plotly_chart(fig, use_container_width=True)
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with col2:
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st.markdown("### 📋 Recent Signals")
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sig_cols = ["time", "close", "rsi", "atr_pct", "position", "signal"]
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recent = data[sig_cols].tail(20).copy()
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recent["position"] = recent["position"].map({1: "LONG", 0: "FLAT", -1: "SHORT"})
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recent["signal"] = recent["signal"].map({1: "🟢 BUY", 0: "⚪", -1: "🔴 SELL"})
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recent = recent.rename(columns={
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"time": "Time", "close": "Price", "rsi": "RSI",
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"atr_pct": "ATR%", "position": "Pos", "signal": "Signal"
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})
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recent["Time"] = recent["Time"].dt.strftime("%m/%d %H:%M")
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st.dataframe(recent, use_container_width=True, hide_index=True)
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st.markdown("---")
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# Row 4: Multi-Pair Heatmap
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st.subheader("🌍 Multi-Pair Comparison")
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with st.spinner("Loading all pairs..."):
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comparison_data = {}
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for p in AVAILABLE_PAIRS:
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try:
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d = load_data(p, "1d", 2)
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if d is not None:
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perf = calculate_performance(d)
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comparison_data[p] = perf
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except Exception:
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continue
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if comparison_data:
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comp_df = pd.DataFrame(comparison_data).T
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comp_df.index.name = "Pair"
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col1, col2 = st.columns([1, 2])
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with col1:
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metrics_select = st.selectbox("Metric", ["total_return_pct", "sharpe_ratio", "max_drawdown_pct", "win_rate_pct"])
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metric_labels = {
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"total_return_pct": "Total Return %",
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"sharpe_ratio": "Sharpe Ratio",
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"max_drawdown_pct": "Max Drawdown %",
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"win_rate_pct": "Win Rate %",
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}
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fig = px.bar(
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comp_df.sort_values(metrics_select, ascending=False),
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y=metrics_select,
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color=metrics_select,
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color_continuous_scale=["red", "yellow", "green"],
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title=f"{metric_labels[metrics_select]} by Pair",
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text_auto=".1f",
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)
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fig.update_layout(
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template="plotly_dark",
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height=400,
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margin=dict(l=0, r=0, t=30, b=0),
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showlegend=False,
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)
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st.plotly_chart(fig, use_container_width=True)
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with col2:
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st.markdown("### 📊 Comparison Table")
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display = comp_df[[
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"total_return_pct", "buy_hold_return_pct",
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"sharpe_ratio", "max_drawdown_pct",
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"win_rate_pct", "num_trades", "exposure_pct"
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]].round(2)
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display.columns = [
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"Return%", "BH Return%", "Sharpe", "Max DD%",
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"Win Rate%", "Trades", "Exposure%"
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]
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st.dataframe(display, use_container_width=True)
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st.markdown("---")
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# Footer
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st.caption("""
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**Forex Quant Monitor** — Data from Yahoo Finance | Strategy: Momentum + Volatility Filter
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Built with Streamlit | Deploy free on streamlit.io/cloud
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""")
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