zhutoutoutousan
605faf5310
Add cluster audit pipeline, united EA updates, brochure generators, and publication hygiene (gitignore, MT5 path desensitization, pre-upload scan). Remove tracked reports, models, and binary artifacts from the repo. Co-authored-by: Cursor <cursoragent@cursor.com>
96 lines
3.4 KiB
Python
96 lines
3.4 KiB
Python
from __future__ import annotations
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import pandas as pd
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import streamlit as st
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from factor_tester.data import load_prices_csv
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from factor_tester.engine import EngineConfig, run_factor_engine, summary_metrics
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from factor_tester.optimize import optimize_grid
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st.set_page_config(page_title="Factor Strategy Tester", layout="wide")
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st.title("Factor Strategy Tester")
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st.caption("MT5-style workflow for factor investing with custom expressions")
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with st.sidebar:
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st.header("Inputs")
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uploaded = st.file_uploader("Upload prices/factors CSV", type=["csv"])
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expr = st.text_area(
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"Factor expression",
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value="(z(momentum_12m) + z(quality) - z(volatility_20d)) / 3",
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height=110,
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)
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freq = st.selectbox("Rebalance", ["D", "W", "M", "Q"], index=2)
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long_q = st.slider("Long quantile", 0.05, 0.50, 0.20, 0.05)
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long_short = st.checkbox("Long-short", value=True)
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short_q = st.slider("Short quantile", 0.05, 0.50, 0.20, 0.05, disabled=not long_short)
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run_btn = st.button("Run Single Test", type="primary", use_container_width=True)
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st.divider()
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st.subheader("Optimization")
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freq_grid = st.multiselect("Freq grid", ["D", "W", "M", "Q"], default=["W", "M"])
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long_grid = st.text_input("Long quantiles", value="0.1,0.2,0.3")
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short_grid = st.text_input("Short quantiles", value="0.1,0.2")
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ls_grid = st.multiselect("Long-short options", [True, False], default=[True])
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opt_btn = st.button("Run Optimization", use_container_width=True)
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if uploaded is None:
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st.info("Upload a CSV to start. Required columns: `date`, `asset`, `close` + factor columns.")
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st.stop()
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try:
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df = load_prices_csv(uploaded)
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except Exception as e:
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st.error(f"Failed to load CSV: {e}")
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st.stop()
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st.write("### Data Preview")
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st.dataframe(df.head(20), use_container_width=True)
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col1, col2, col3, col4 = st.columns(4)
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if run_btn:
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try:
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cfg = EngineConfig(
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factor_expression=expr,
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rebalance_frequency=freq,
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long_quantile=float(long_q),
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short_quantile=float(short_q if long_short else 0.0),
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long_short=bool(long_short),
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)
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curve, detail = run_factor_engine(df, cfg)
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m = summary_metrics(curve)
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col1.metric("CAGR", f"{m['cagr']:.2%}")
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col2.metric("Sharpe", f"{m['sharpe']:.2f}")
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col3.metric("Max Drawdown", f"{m['max_dd']:.2%}")
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col4.metric("Total Return", f"{m['total_return']:.2%}")
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st.write("### Equity Curve")
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st.line_chart(curve.set_index("date")["equity"])
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st.write("### Daily Returns")
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st.line_chart(curve.set_index("date")["portfolio_ret"])
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st.write("### Engine Detail (tail)")
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st.dataframe(detail.tail(50), use_container_width=True)
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except Exception as e:
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st.error(f"Backtest failed: {e}")
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if opt_btn:
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try:
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lq = [float(x.strip()) for x in long_grid.split(",") if x.strip()]
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sq = [float(x.strip()) for x in short_grid.split(",") if x.strip()]
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results = optimize_grid(
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df=df,
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factor_expression=expr,
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freqs=freq_grid or ["M"],
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long_qs=lq or [0.2],
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short_qs=sq or [0.2],
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long_short_options=ls_grid or [True],
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)
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st.write("### Optimization Results")
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st.dataframe(results, use_container_width=True)
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except Exception as e:
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st.error(f"Optimization failed: {e}")
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