Portfolio builder/master improvements, batch backtest fixes, parser FIFO fix, theme updates
This commit is contained in:
unknown
+100
-13
@@ -476,6 +476,7 @@ def _init_state():
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"pm_cancel": False,
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"pm_thread_results": None,
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"pm_progress_q": None,
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"pm_uploader_key": 0,
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}.items():
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if k not in st.session_state:
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st.session_state[k] = v
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@@ -498,16 +499,28 @@ def render():
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padding:10px 14px;font-size:13px;color:#FFB347;margin:8px 0}
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</style>""", unsafe_allow_html=True)
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st.markdown('<p class="pm-title">🏆 Portfolio Master</p>', unsafe_allow_html=True)
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st.markdown('<p class="pm-sub">Automated portfolio construction — composite scoring, greedy & Monte Carlo search</p>',
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unsafe_allow_html=True)
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_tc1, _tc2 = st.columns([8, 1])
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with _tc1:
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st.markdown('<p class="pm-title">🏆 Portfolio Master</p>', unsafe_allow_html=True)
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st.markdown('<p class="pm-sub">Automated portfolio construction — composite scoring, greedy & Monte Carlo search</p>',
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unsafe_allow_html=True)
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with _tc2:
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st.markdown("<br>", unsafe_allow_html=True)
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if st.button("🗑 Clear", key="pm_clear_session", help="Clear all files and results to start fresh"):
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st.session_state.pm_uploader_key = st.session_state.get("pm_uploader_key", 0) + 1
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for _k in ["pm_files","pm_custom_names","pm_results","pm_running",
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"pm_cancel","pm_thread_results","pm_progress_q","pm_cancel_event"]:
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if _k in st.session_state:
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del st.session_state[_k]
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st.rerun()
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# ── Upload ───────────────────────────────────────────────────────────────
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with st.expander("📂 Upload Backtest Files",
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expanded=not bool(st.session_state.pm_files)):
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st.caption("Accepts `.htm` · `.html` · `.csv`")
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uploaded = st.file_uploader(
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"Select files", type=None, accept_multiple_files=True, key="pm_uploader",
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"Select files", type=None, accept_multiple_files=True,
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key=f"pm_uploader_{st.session_state.pm_uploader_key}",
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)
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if uploaded:
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uploaded = [f for f in uploaded
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@@ -568,7 +581,8 @@ def render():
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st.markdown('<div class="sh">Composite Score Weights</div>', unsafe_allow_html=True)
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st.caption("Weights are normalised automatically — they don't need to sum to 1.")
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wc1, wc2, wc3 = st.columns(3)
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wc1, wc2, wc3, wc4 = st.columns([2, 2, 2, 3])
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w_retdd = wc1.slider("Ret/DD", 0, 100, 35, key="pm_w_retdd")
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w_stab = wc1.slider("Stability (R²)", 0, 100, 25, key="pm_w_stab")
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w_stag = wc2.slider("Stagnation % ↓", 0, 100, 20, key="pm_w_stag",
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@@ -579,6 +593,28 @@ def render():
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w_div = wc3.slider("Diversity Bonus", 0, 100, 5, key="pm_w_div",
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help="Rewards portfolios trading different symbols / sessions")
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with wc4:
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import os as _osw, re as _rew
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_cfgw = _osw.path.join(_osw.path.dirname(_osw.path.abspath(__file__)), ".streamlit", "config.toml")
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_lightw = False
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if _osw.path.isfile(_cfgw):
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_mw = _rew.search(r'base\s*=\s*"([^"]*)"', open(_cfgw).read())
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if _mw: _lightw = _mw.group(1) == "light"
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_wbg = "#f0f2f6" if _lightw else "#131720"
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_wbdr = "#d0d4dc" if _lightw else "#1E2535"
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_wtxt = "#555e70" if _lightw else "#8899AA"
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_wlbl = "#1a1a2e" if _lightw else "#CDD6F4"
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st.markdown(f"""
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<div style="background:{_wbg};border:1px solid {_wbdr};border-radius:8px;
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padding:10px 14px;font-size:11px;color:{_wtxt};line-height:1.8;margin-top:4px">
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<b style="color:{_wlbl}">Ret/DD</b> — Net profit ÷ max drawdown. Primary return efficiency metric. Most important for risk-adjusted performance.<br>
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<b style="color:{_wlbl}">Stability (R²)</b> — How straight the equity curve is. High R² means consistent gains without large swings.<br>
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<b style="color:{_wlbl}">Stagnation ↓</b> — Time spent below a previous equity high, as % of total period. Lower = better; score is inverted.<br>
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<b style="color:{_wlbl}">Win Rate</b> — Percentage of trades that are profitable. Higher win rate reduces psychological drawdown pressure.<br>
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<b style="color:{_wlbl}">Growth Quality</b> — Combines equity curve slope with R². Rewards portfolios that rise steadily, not just flat and stable.<br>
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<b style="color:{_wlbl}">Diversity Bonus</b> — Rewards combinations trading different symbols and/or different hours of the day.
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</div>""", unsafe_allow_html=True)
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total_w = w_retdd + w_stab + w_stag + w_wr + w_gq + w_div or 1
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weights = {
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"ret_dd": w_retdd / total_w,
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@@ -901,14 +937,37 @@ def render():
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# ── Summary table ─────────────────────────────────────────────────
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st.markdown(f"##### Top {len(results)} Portfolios")
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st.markdown("""
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<div style="background:#131720;border:1px solid #1E2535;border-radius:8px;padding:12px 16px;font-size:12px;color:#8899AA;margin-bottom:12px;line-height:1.7">
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<b style="color:#CDD6F4">Score</b> — Composite ranking (0–1000). Higher is better. Weighted blend of the metrics below based on your sliders.<br>
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<b style="color:#CDD6F4">Stability</b> — How straight the equity curve is (0–100). 100 = perfectly straight rising line. Computed as R² of linear regression on the equity curve.<br>
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<b style="color:#CDD6F4">Growth Quality</b> — Combines curve straightness with upward slope. Rewards portfolios that rise consistently, not just ones that are flat and stable.<br>
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<b style="color:#CDD6F4">Diversity</b> — How different the strategies are from each other (0–100), based on symbol variety and trading session overlap. 100 = completely different symbols and hours.<br>
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<b style="color:#CDD6F4">Avg Corr</b> — Average pairwise correlation of daily P&L across all strategy pairs. Lower is better — strategies that don't move together reduce portfolio drawdown.<br>
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<b style="color:#CDD6F4">Avg Cond Corr</b> — Same correlation computed only on days when the portfolio is in drawdown. Strategies that decorrelate during losses are more valuable than those that only decorrelate on good days.
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import os as _os2, re as _re3
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_cfg2 = _os2.path.join(_os2.path.dirname(_os2.path.abspath(__file__)), ".streamlit", "config.toml")
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_light2 = False
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if _os2.path.isfile(_cfg2):
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_m2 = _re3.search(r'base\s*=\s*"([^"]*)"', open(_cfg2).read())
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if _m2: _light2 = _m2.group(1) == "light"
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_desc_bg = "#f0f2f6" if _light2 else "#131720"
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_desc_border = "#d0d4dc" if _light2 else "#1E2535"
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_desc_text = "#555e70" if _light2 else "#8899AA"
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_desc_label = "#1a1a2e" if _light2 else "#CDD6F4"
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_desc_thresh = lambda good, warn: (
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f'<span style="color:#34C27A">{good}</span> | ' +
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f'<span style="color:#f77f00">{warn}</span> | ' +
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f'<span style="color:#E05555">below = poor</span>'
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)
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st.markdown(f"""
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<div style="background:{_desc_bg};border:1px solid {_desc_border};border-radius:8px;padding:12px 16px;font-size:12px;color:{_desc_text};margin-bottom:12px;line-height:1.9">
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<b style="color:{_desc_label}">Score</b> — Composite ranking (0–1000). Higher is better. Weighted blend of the metrics below based on your sliders.
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<span style="color:#34C27A">≥700 = strong</span> | <span style="color:#f77f00">400–700 = average</span> | <span style="color:#E05555"><400 = weak</span><br>
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<b style="color:{_desc_label}">Ret/DD</b> — Net profit divided by max drawdown. Measures return efficiency per unit of risk.
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<span style="color:#34C27A">≥5 = strong</span> | <span style="color:#f77f00">2–5 = average</span> | <span style="color:#E05555"><2 = weak</span><br>
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<b style="color:{_desc_label}">Stability</b> — How straight the equity curve is (0–100). 100 = perfectly straight rising line. R² of linear regression on the equity curve.
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<span style="color:#34C27A">≥70 = strong</span> | <span style="color:#f77f00">40–70 = average</span> | <span style="color:#E05555"><40 = weak</span><br>
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<b style="color:{_desc_label}">Growth Quality</b> — Combines curve straightness with upward slope. Rewards portfolios that rise consistently, not just ones that are flat and stable.
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<span style="color:#34C27A">≥50 = strong</span> | <span style="color:#f77f00">20–50 = average</span> | <span style="color:#E05555"><20 = weak</span><br>
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<b style="color:{_desc_label}">Diversity</b> — How different the strategies are from each other (0–100), based on symbol variety and trading session overlap. 100 = completely different.
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<span style="color:#34C27A">≥60 = strong</span> | <span style="color:#f77f00">30–60 = average</span> | <span style="color:#E05555"><30 = low diversity</span><br>
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<b style="color:{_desc_label}">Avg Corr</b> — Average pairwise correlation of daily P&L. Lower is better — strategies that don't move together reduce portfolio drawdown.
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<span style="color:#34C27A">≤0.20 = low (good)</span> | <span style="color:#f77f00">0.20–0.50 = moderate</span> | <span style="color:#E05555">>0.50 = high (bad)</span><br>
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<b style="color:{_desc_label}">Avg Cond Corr</b> — Same correlation computed only on drawdown days. Strategies that decorrelate during losses are more valuable.
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<span style="color:#34C27A">≤0.20 = low (good)</span> | <span style="color:#f77f00">0.20–0.50 = moderate</span> | <span style="color:#E05555">>0.50 = high (bad)</span>
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</div>
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""", unsafe_allow_html=True)
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@@ -958,6 +1017,20 @@ def render():
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neg_cols = [c for c in ["Max DD ($)","Max DD (%)","Avg Loss ($)","Avg Corr","Avg Cond Corr"]
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if c in res_df.columns]
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def _grade(val, good, avg):
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"""Return green/orange/red based on good/avg thresholds (higher=better)."""
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if not isinstance(val, (int, float)): return ""
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if val >= good: return "background-color:rgba(52,194,122,0.15);color:#34C27A"
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if val >= avg: return "background-color:rgba(247,127,0,0.12);color:#f77f00"
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return "background-color:rgba(220,50,50,0.12);color:#E05555"
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def _grade_inv(val, good, avg):
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"""Return green/orange/red — lower is better (correlation)."""
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if not isinstance(val, (int, float)): return ""
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if val <= good: return "background-color:rgba(52,194,122,0.15);color:#34C27A"
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if val <= avg: return "background-color:rgba(247,127,0,0.12);color:#f77f00"
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return "background-color:rgba(220,50,50,0.12);color:#E05555"
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styled = (
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res_df.style.format(fmt)
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.map(_cc, subset=pos_cols if pos_cols else [])
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@@ -965,6 +1038,20 @@ def render():
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subset=neg_cols if neg_cols else [])
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.map(lambda v: _cc(v, 1.0),
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subset=["Profit Factor"] if "Profit Factor" in res_df.columns else [])
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.map(lambda v: _grade(v, 700, 400),
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subset=["Score"] if "Score" in res_df.columns else [])
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.map(lambda v: _grade(v, 5, 2),
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subset=["Ret/DD"] if "Ret/DD" in res_df.columns else [])
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.map(lambda v: _grade(v, 70, 40),
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subset=["Stability"] if "Stability" in res_df.columns else [])
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.map(lambda v: _grade(v, 50, 20),
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subset=["Growth Quality"] if "Growth Quality" in res_df.columns else [])
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.map(lambda v: _grade(v, 60, 30),
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subset=["Diversity"] if "Diversity" in res_df.columns else [])
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.map(lambda v: _grade_inv(v, 0.20, 0.50),
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subset=["Avg Corr"] if "Avg Corr" in res_df.columns else [])
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.map(lambda v: _grade_inv(v, 0.20, 0.50),
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subset=["Avg Cond Corr"] if "Avg Cond Corr" in res_df.columns else [])
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)
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st.dataframe(styled, use_container_width=True, hide_index=True)
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