""" view_portfolio_builder.py — Portfolio Builder page for MT5 Tools Tabs: Overview | Trades | Equity Chart | Strategies | Portfolios | What-If """ import streamlit as st import pandas as pd import numpy as np import plotly.graph_objects as go from plotly.subplots import make_subplots import io, importlib, sys, os # ───────────────────────────────────────────────────────────────────────────── # Parser # ───────────────────────────────────────────────────────────────────────────── def _get_parser(): if "mt5_parser" in sys.modules: return importlib.reload(sys.modules["mt5_parser"]) import mt5_parser return mt5_parser # ───────────────────────────────────────────────────────────────────────────── # Parse & normalise # ───────────────────────────────────────────────────────────────────────────── def _parse_uploaded(file_obj): try: parser = _get_parser() raw = file_obj.read() result = parser.detect_and_parse(raw) return result[0] if isinstance(result, tuple) else result except Exception as e: st.error(f"Failed to parse **{file_obj.name}**: {e}") return None def _ensure_columns(df: pd.DataFrame, label: str) -> pd.DataFrame: col_map = {} def _first(targets, dest): for c in targets: if c in df.columns and dest not in col_map.values(): col_map[c] = dest return _first(["open_time","Open time","Open time ($)","Time"], "open_time") _first(["close_time","Close time"], "close_time") _first(["symbol","Symbol"], "symbol") _first(["type","Type","Direction"], "type") _first(["net_profit","P/L in money","Profit","profit"], "net_profit") _first(["open_price","Open price","Price"], "open_price") _first(["close_price","Close price"], "close_price") _first(["volume","Volume","Size","size"], "volume") _first(["commission","Commission"], "commission") _first(["swap","Swap"], "swap") _first(["comment","Comment"], "comment") df = df.rename(columns=col_map) if "net_profit" not in df.columns: for c in ["profit","Profit","P/L"]: if c in df.columns: comm = pd.to_numeric(df.get("commission", 0), errors="coerce").fillna(0) swap_ = pd.to_numeric(df.get("swap", 0), errors="coerce").fillna(0) df["net_profit"] = pd.to_numeric(df[c], errors="coerce").fillna(0) + comm + swap_ break for tc in ["open_time","close_time"]: if tc in df.columns: df[tc] = pd.to_datetime(df[tc], dayfirst=True, errors="coerce") if "net_profit" in df.columns: df["net_profit"] = pd.to_numeric(df["net_profit"], errors="coerce").fillna(0) df["win"] = df["net_profit"] > 0 df["_strategy"] = label return df # ───────────────────────────────────────────────────────────────────────────── # Lot-size scaling — applies a multiplier to net_profit of a strategy copy # ───────────────────────────────────────────────────────────────────────────── def _scale_df(df: pd.DataFrame, multiplier: float) -> pd.DataFrame: """Return a copy of df with net_profit scaled by multiplier.""" out = df.copy() out["net_profit"] = out["net_profit"] * multiplier if "win" in out.columns: out["win"] = out["net_profit"] > 0 return out def _get_effective_dfs(strategy_dfs: dict, lot_overrides: dict) -> dict: """Return strategy_dfs with lot-scaled copies substituted where overrides exist.""" result = {} for label, df in strategy_dfs.items(): mult = lot_overrides.get(label, 1.0) result[label] = _scale_df(df, mult) if mult != 1.0 else df return result # ───────────────────────────────────────────────────────────────────────────── # Combine # ───────────────────────────────────────────────────────────────────────────── def _combine(dfs_dict: dict, deposit: float) -> pd.DataFrame: if not dfs_dict: return pd.DataFrame() combined = pd.concat([d.copy() for d in dfs_dict.values()], ignore_index=True) if "close_time" in combined.columns: combined = combined.sort_values("close_time").reset_index(drop=True) if "net_profit" in combined.columns: combined["equity"] = deposit + combined["net_profit"].cumsum() return combined def _get_active_df(view_mode: str, eff_dfs: dict, portfolios: dict, deposit: float): if view_mode == "Portfolio (all)": return _combine(eff_dfs, deposit), "Portfolio (all)" if view_mode in portfolios: members = {k: eff_dfs[k] for k in portfolios[view_mode] if k in eff_dfs} return _combine(members, deposit), view_mode if view_mode in eff_dfs: df = eff_dfs[view_mode].copy() if "close_time" in df.columns: df = df.sort_values("close_time").reset_index(drop=True) if "net_profit" in df.columns: df["equity"] = deposit + df["net_profit"].cumsum() return df, view_mode return pd.DataFrame(), view_mode # ───────────────────────────────────────────────────────────────────────────── # Stats # ───────────────────────────────────────────────────────────────────────────── def _calc_stats(df: pd.DataFrame, deposit: float) -> dict: s = {} if df.empty or "net_profit" not in df.columns: return s profits = df["net_profit"].fillna(0) s["num_trades"] = len(df) s["gross_profit"] = float(profits[profits > 0].sum()) s["gross_loss"] = float(profits[profits < 0].sum()) s["net_profit"] = float(profits.sum()) s["win_count"] = int((profits > 0).sum()) s["loss_count"] = int((profits <= 0).sum()) s["win_rate"] = s["win_count"] / s["num_trades"] * 100 if s["num_trades"] else 0 s["avg_win"] = float(profits[profits > 0].mean()) if s["win_count"] else 0 s["avg_loss"] = float(profits[profits < 0].mean()) if s["loss_count"] else 0 s["profit_factor"] = s["gross_profit"] / abs(s["gross_loss"]) if s["gross_loss"] else float("inf") s["avg_trade"] = float(profits.mean()) # Avg lot size if "volume" in df.columns: s["avg_lot"] = float(pd.to_numeric(df["volume"], errors="coerce").mean()) else: s["avg_lot"] = 0.0 eq = deposit + profits.cumsum() rm = eq.cummax() dd = eq - rm s["max_dd"] = float(dd.min()) s["max_dd_pct"] = float(dd.min() / deposit * 100) s["ret_dd_ratio"] = s["net_profit"] / abs(s["max_dd"]) if s["max_dd"] else 0 ws = (profits > 0).astype(int).tolist() cw = cl = mcw = mcl = 0 for w in ws: if w: cw += 1; cl = 0 else: cl += 1; cw = 0 mcw = max(mcw, cw); mcl = max(mcl, cl) s["max_consec_wins"] = mcw s["max_consec_losses"] = mcl if "close_time" in df.columns: vc = df["close_time"].dropna() vo = df["open_time"].dropna() if "open_time" in df.columns else vc if not vc.empty: s["start_date"] = vo.min() if not vo.empty else vc.min() s["end_date"] = vc.max() days = max((s["end_date"] - s["start_date"]).days, 1) s["years"] = days / 365.25 s["yearly_avg_profit"] = s["net_profit"] / s["years"] s["monthly_avg_profit"] = s["net_profit"] / max(days / 30.44, 1) s["cagr"] = ((deposit + s["net_profit"]) / deposit) ** (1 / s["years"]) - 1 if "close_time" in df.columns: eq_ts = df[["close_time","net_profit"]].dropna().sort_values("close_time").copy() if not eq_ts.empty: eq_ts["cum"] = deposit + eq_ts["net_profit"].cumsum() eq_ts["date"] = eq_ts["close_time"].dt.date dly = eq_ts.groupby("date")["cum"].last().reset_index() peak = float(dly["cum"].iloc[0]); stag_start = dly["date"].iloc[0]; max_stag = 0 for _, r in dly.iterrows(): if float(r["cum"]) > peak: peak = float(r["cum"]); stag_start = r["date"] else: max_stag = max(max_stag, (r["date"] - stag_start).days) s["max_stagnation_days"] = max_stag # Monthly tables — both $ and % if "close_time" in df.columns: mdf = df[["close_time","net_profit"]].dropna().copy() mdf["year"] = mdf["close_time"].dt.year mdf["month"] = mdf["close_time"].dt.month monthly = mdf.groupby(["year","month"])["net_profit"].sum().reset_index() pivot_d = monthly.pivot(index="year", columns="month", values="net_profit").fillna(0) pivot_d.columns = [pd.Timestamp(2000, int(m), 1).strftime("%b") for m in pivot_d.columns] pivot_d["YTD"] = pivot_d.sum(axis=1) s["monthly_table_dollar"] = pivot_d # % — each month relative to deposit pivot_p = pivot_d.copy() for col in pivot_p.columns: pivot_p[col] = pivot_p[col] / deposit * 100 s["monthly_table_pct"] = pivot_p return s # ───────────────────────────────────────────────────────────────────────────── # Chart helpers # ───────────────────────────────────────────────────────────────────────────── COLORS = ["#4C8EF5","#F5A623","#7ED321","#BD10E0", "#9B59B6","#1ABC9C","#E67E22","#FF6B9D"] def _smooth(y: pd.Series, window: int) -> pd.Series: if window <= 1: return y return y.rolling(window=window, min_periods=1, center=True).mean() def _add_stagnation_vrect(fig, df: pd.DataFrame, deposit: float): eq_ts = df[["close_time","net_profit"]].dropna().sort_values("close_time").copy() if eq_ts.empty: return eq_ts["cum"] = deposit + eq_ts["net_profit"].cumsum() peak = float(eq_ts["cum"].iloc[0]); stag_start = eq_ts["close_time"].iloc[0] max_days = 0; best_s = stag_start; best_e = stag_start for _, r in eq_ts.iterrows(): if float(r["cum"]) > peak: days = (r["close_time"] - stag_start).days if days > max_days: max_days = days; best_s = stag_start; best_e = r["close_time"] peak = float(r["cum"]); stag_start = r["close_time"] if max_days > 0: fig.add_vrect(x0=best_s, x1=best_e, fillcolor="rgba(255,160,80,0.10)", line_width=0, annotation_text=f"Max stagnation: {max_days}d", annotation_position="top left", annotation_font_size=11, annotation_font_color="#FFB366", row=1, col=1) def _build_equity_chart( df: pd.DataFrame, deposit: float, eff_dfs: dict, portfolios: dict, active_label: str, chart_view: str, smooth_window: int, show_stagnation: bool, date_from, date_to, selected_strategies: list, selected_portfolio: str = None, ): fig = make_subplots( rows=3, cols=1, shared_xaxes=True, row_heights=[0.62, 0.19, 0.19], vertical_spacing=0.02, subplot_titles=("", "Drawdown from Peak ($)", "Daily P&L ($)"), ) all_dd_frames = [] all_daily_frames = [] # Compute the global time span across all series being plotted so every # line can be extended to span the full x-axis width. _all_times = [] for _sdf in eff_dfs.values(): if "close_time" in _sdf.columns: _all_times.append(pd.to_datetime(_sdf["close_time"]).dt.tz_localize(None).dropna()) if not df.empty and "close_time" in df.columns: _all_times.append(pd.to_datetime(df["close_time"]).dt.tz_localize(None).dropna()) _global_min = min(s.min() for s in _all_times) if _all_times else None _global_max = max(s.max() for s in _all_times) if _all_times else None # Clamp to date filter if set if date_from and _global_min is not None: _global_min = max(_global_min, pd.Timestamp(date_from)) if date_to and _global_max is not None: _global_max = min(_global_max, pd.Timestamp(date_to) + pd.Timedelta(days=1)) def _plot_series(times: pd.Series, profits: pd.Series, name: str, color: str, width: float): times = times.reset_index(drop=True) profits = profits.reset_index(drop=True) eq_full = deposit + profits.cumsum() rm_full = eq_full.cummax() dd_full = eq_full - rm_full times_dt = pd.to_datetime(times).dt.tz_localize(None) mask = pd.Series([True] * len(times_dt), dtype=bool) if date_from: mask &= times_dt >= pd.Timestamp(date_from) if date_to: mask &= times_dt <= pd.Timestamp(date_to) + pd.Timedelta(days=1) times_f = times_dt[mask].reset_index(drop=True) eq_f = eq_full[mask].reset_index(drop=True) dd_f = dd_full[mask].reset_index(drop=True) if eq_f.empty: return all_dd_frames.append(pd.DataFrame({"t": times_f, "dd": dd_f})) # Daily P&L — sum net_profit per day within the filtered window profits_f = profits[mask].reset_index(drop=True) daily_pnl = (pd.DataFrame({"t": times_f, "pnl": profits_f}) .assign(date=lambda x: x["t"].dt.normalize()) .groupby("date")["pnl"].sum() .reset_index() .rename(columns={"date": "t"})) all_daily_frames.append(daily_pnl) eq_disp = _smooth(eq_f, smooth_window) # Extend line to global span so all series fill the full x-axis if _global_min is not None and len(times_f) > 0 and times_f.iloc[0] > _global_min: times_f = pd.concat([pd.Series([_global_min]), times_f], ignore_index=True) eq_disp = pd.concat([pd.Series([eq_disp.iloc[0]]), eq_disp], ignore_index=True) if _global_max is not None and len(times_f) > 0 and times_f.iloc[-1] < _global_max: times_f = pd.concat([times_f, pd.Series([_global_max])], ignore_index=True) eq_disp = pd.concat([eq_disp, pd.Series([eq_disp.iloc[-1]])], ignore_index=True) fig.add_trace(go.Scatter( x=times_f, y=eq_disp, name=name, line=dict(color=color, width=width), mode="lines", connectgaps=True, hovertemplate=f"{name}
%{{x|%d %b %Y}}
${{y:,.2f}}", ), row=1, col=1) if chart_view == "Portfolio": # Show the selected portfolio / all as one combined line if not df.empty and "close_time" in df.columns and "net_profit" in df.columns: _plot_series(df["close_time"], df["net_profit"], active_label, COLORS[0], 2.0) if show_stagnation: _add_stagnation_vrect(fig, df, deposit) elif chart_view == "Portfolio+Individual": # Combined line + each member underneath if not df.empty and "close_time" in df.columns and "net_profit" in df.columns: _plot_series(df["close_time"], df["net_profit"], f"{active_label} (combined)", "#FFFFFF", 2.5) if show_stagnation: _add_stagnation_vrect(fig, df, deposit) for i, label in enumerate(selected_strategies): if label not in eff_dfs: continue sdf = eff_dfs[label] if "close_time" not in sdf.columns or "net_profit" not in sdf.columns: continue sdf_s = sdf.sort_values("close_time") _plot_series(sdf_s["close_time"], sdf_s["net_profit"], label, COLORS[i % len(COLORS)], 1.2) else: # Individual for i, label in enumerate(selected_strategies): if label not in eff_dfs: continue sdf = eff_dfs[label] if "close_time" not in sdf.columns or "net_profit" not in sdf.columns: continue sdf_s = sdf.sort_values("close_time") _plot_series(sdf_s["close_time"], sdf_s["net_profit"], label, COLORS[i % len(COLORS)], 1.5) # Row 2 — cumulative drawdown from peak if all_dd_frames: dd_all = pd.concat(all_dd_frames).sort_values("t").reset_index(drop=True) dd_agg = dd_all.groupby("t")["dd"].min().reset_index() fig.add_trace(go.Scatter( x=dd_agg["t"], y=dd_agg["dd"], name="Peak DD", fill="tozeroy", fillcolor="rgba(220,50,50,0.30)", line=dict(color="rgba(220,50,50,0.75)", width=1), mode="lines", showlegend=False, hovertemplate="Peak DD: $%{y:,.2f}", ), row=2, col=1) # Row 3 — daily P&L bars if all_daily_frames: daily_all = pd.concat(all_daily_frames).groupby("t")["pnl"].sum().reset_index() pos = daily_all["pnl"].clip(lower=0) neg = daily_all["pnl"].clip(upper=0) # Green bars for positive days fig.add_trace(go.Bar( x=daily_all["t"], y=pos, name="Daily gain", marker_color="rgba(52,194,122,0.70)", showlegend=False, hovertemplate="Daily P&L: $%{y:,.2f}", ), row=3, col=1) # Red bars for negative days fig.add_trace(go.Bar( x=daily_all["t"], y=neg, name="Daily loss", marker_color="rgba(220,50,50,0.70)", showlegend=False, hovertemplate="Daily P&L: $%{y:,.2f}", ), row=3, col=1) fig.update_layout( height=1240, margin=dict(l=60,r=20,t=24,b=10), paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="#0E1117", legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="left", x=0, font=dict(size=11)), hovermode="x unified", bargap=0, barmode="overlay", ) # Force x-axis range to match the selected date window x_min = pd.Timestamp(date_from) if date_from else None x_max = pd.Timestamp(date_to) + pd.Timedelta(days=1) if date_to else None fig.update_xaxes( gridcolor="#1E2130", zeroline=False, showspikes=True, spikecolor="#445", spikethickness=1, range=[x_min, x_max] if x_min and x_max else None, ) fig.update_yaxes(gridcolor="#1E2130", zeroline=False) fig.update_yaxes(title_text="Equity ($)", row=1, col=1, tickprefix="$") fig.update_yaxes(title_text="Peak DD ($)", row=2, col=1, tickprefix="$") fig.update_yaxes(title_text="Daily P&L ($)", row=3, col=1, tickprefix="$") for ann in fig.layout.annotations: ann.font.size = 11 ann.font.color = "#6C7A8D" return fig # ───────────────────────────────────────────────────────────────────────────── # Monthly HTML table (supports $ or %) # ───────────────────────────────────────────────────────────────────────────── def _monthly_html(pivot: pd.DataFrame, mode: str = "$") -> str: order = ["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec","YTD"] cols = [c for c in order if c in pivot.columns] pivot = pivot[cols] rows = [] for year, row in pivot.iterrows(): cells = [f"{year}"] for col in cols: v = row.get(col, 0) if pd.isna(v) or v == 0: cls = "z" txt = "0" elif v > 0: cls = "p" txt = f"{v:,.2f}%" if mode == "%" else f"{v:,.2f}" else: cls = "n" txt = f"{v:,.2f}%" if mode == "%" else f"{v:,.2f}" cells.append(f"{txt}") rows.append("" + "".join(cells) + "") hdr = "Year" + "".join(f"{c}" for c in cols) + "" return ( "" f"{hdr}{''.join(rows)}
" ) # ───────────────────────────────────────────────────────────────────────────── # Strategy comparison table # ───────────────────────────────────────────────────────────────────────────── def _strategy_table(eff_dfs: dict, deposit: float, lot_overrides: dict) -> pd.DataFrame: rows = [] for label, df in eff_dfs.items(): s = _calc_stats(df, deposit) pf = s.get("profit_factor", 0) mult = lot_overrides.get(label, 1.0) # Avg lot from original (unscaled) volume; if scaled show effective avg base_avg_lot = s.get("avg_lot", 0.0) rows.append({ "Strategy": label, "Lot ×": round(mult, 2), "Avg Lot": round(base_avg_lot, 4), "Trades": s.get("num_trades", 0), "Net Profit ($)": round(s.get("net_profit", 0), 2), "Win Rate (%)": round(s.get("win_rate", 0), 2), "Profit Factor": round(pf, 2) if pf != float("inf") else 999.0, "Max DD ($)": round(s.get("max_dd", 0), 2), "Max DD (%)": round(s.get("max_dd_pct", 0), 2), "Ret/DD": round(s.get("ret_dd_ratio", 0), 2), "Avg Trade ($)": round(s.get("avg_trade", 0), 2), "Stagnation (d)": s.get("max_stagnation_days", 0), "Start": str(s.get("start_date",""))[:10], "End": str(s.get("end_date",""))[:10], }) return pd.DataFrame(rows) # ───────────────────────────────────────────────────────────────────────────── # Session state # ───────────────────────────────────────────────────────────────────────────── def _init_state(): for k, v in { "pb_uploaded_files": {}, "pb_portfolios": {}, "pb_lot_overrides": {}, # label → float multiplier "pb_deposit": 10000.0, }.items(): if k not in st.session_state: st.session_state[k] = v # ───────────────────────────────────────────────────────────────────────────── # Render # ───────────────────────────────────────────────────────────────────────────── def render(): _init_state() st.markdown("""""", unsafe_allow_html=True) st.markdown('

📊 Portfolio Builder

', unsafe_allow_html=True) st.markdown('

Combine MT5 backtest reports into multi-strategy portfolios

', unsafe_allow_html=True) # ── Upload panel ───────────────────────────────────────────────────────── # [8] Show only htm/html/csv in the help text; type=None + manual filter # because Streamlit on Windows sometimes chokes on type=["htm"] with st.expander("📂 Upload Strategy Reports", expanded=not bool(st.session_state.pb_uploaded_files)): st.caption("Accepts: `.htm` · `.html` · `.csv` — other file types are ignored.") uploaded = st.file_uploader( "Select HTM or CSV files (PNG and other files in the same folder are ignored)", type=None, accept_multiple_files=True, key="pb_uploader", ) if uploaded: rejected = [f.name for f in uploaded if not f.name.lower().endswith((".htm",".html",".csv"))] if rejected: st.warning(f"Ignored {len(rejected)} unsupported file(s): " + ", ".join(rejected[:5]) + (" …" if len(rejected) > 5 else "")) uploaded = [f for f in uploaded if f.name.lower().endswith((".htm",".html",".csv"))] for f in uploaded: stem = os.path.splitext(f.name)[0] if stem not in st.session_state.pb_uploaded_files: df = _parse_uploaded(f) if df is not None: df = _ensure_columns(df, stem) st.session_state.pb_uploaded_files[stem] = df st.success(f"✅ **{stem}** — {len(df):,} trades") if st.session_state.pb_uploaded_files: st.markdown("**Loaded strategies:**") to_remove = [] for label in list(st.session_state.pb_uploaded_files): c1, c2 = st.columns([6,1]) c1.markdown(f"📈 {label}", unsafe_allow_html=True) if c2.button("✕", key=f"rm_{label}"): to_remove.append(label) for k in to_remove: del st.session_state.pb_uploaded_files[k] st.session_state.pb_lot_overrides.pop(k, None) for pn in st.session_state.pb_portfolios: if k in st.session_state.pb_portfolios[pn]: st.session_state.pb_portfolios[pn].remove(k) st.rerun() strategy_dfs: dict = st.session_state.pb_uploaded_files portfolios: dict = st.session_state.pb_portfolios lot_overrides: dict = st.session_state.pb_lot_overrides if not strategy_dfs: st.info("Upload one or more strategy reports above to get started.") return # Effective DFs (with lot scaling applied) eff_dfs = _get_effective_dfs(strategy_dfs, lot_overrides) # ── View selector + deposit ─────────────────────────────────────────────── view_options = (["Portfolio (all)"] + [f"Portfolio: {p}" for p in portfolios] + list(strategy_dfs.keys())) label_map = {"Portfolio (all)": "Portfolio (all)"} for p in portfolios: label_map[f"Portfolio: {p}"] = p for s in strategy_dfs: label_map[s] = s sc1, sc2 = st.columns([4, 2]) view_sel = sc1.selectbox("View", view_options, key="pb_view_sel") deposit = sc2.number_input("Initial Deposit ($)", min_value=100.0, max_value=10_000_000.0, value=st.session_state.pb_deposit, step=1000.0, format="%.2f", key="pb_deposit_input") st.session_state.pb_deposit = deposit view_mode = label_map.get(view_sel, view_sel) df, active_label = _get_active_df(view_mode, eff_dfs, portfolios, deposit) # ── Shared date-range slider (used by Overview + Trades) ─────────────────────────── import datetime as _dt _has_dates = not df.empty and "close_time" in df.columns if _has_dates: _all_dates = pd.to_datetime(df["close_time"]).dt.tz_localize(None).dropna() _gmin = _all_dates.min().date() _gmax = _all_dates.max().date() _total_days = (_gmax - _gmin).days _step = max(1, _total_days // 500) _date_options = [_gmin + _dt.timedelta(days=i) for i in range(0, _total_days + 1, _step)] if _date_options[-1] != _gmax: _date_options.append(_gmax) else: _gmin = _gmax = None _date_options = [] def _date_slider(key_prefix): if not _has_dates or _gmin == _gmax or len(_date_options) < 2: return _gmin, _gmax sel = st.select_slider( "Date range", options=_date_options, value=(_gmin, _gmax), format_func=lambda d: d.strftime("%d %b %Y"), key=f"{key_prefix}_dslider", ) return sel[0], sel[1] # ── Overview filters (only shown for multi-strategy views) ─────────────── # Determine if this is a portfolio/all view with multiple strategies _is_multi = view_mode in ("Portfolio (all)",) or view_mode in portfolios ov_df = df # default — filtered below if _is_multi if not df.empty: ov_date_from, ov_date_to = _date_slider("pb_ov") strat_labels = sorted(df["_strategy"].dropna().unique().tolist()) \ if "_strategy" in df.columns else [] sym_labels = sorted(df["symbol"].dropna().unique().tolist()) \ if "symbol" in df.columns else [] if _is_multi: strat_numbered = {f"{i+1} \u2014 {s}": s for i, s in enumerate(strat_labels)} fc1, fc2 = st.columns(2) sel_strat_nums = fc1.multiselect( "Filter strategies", list(strat_numbered.keys()), default=list(strat_numbered.keys()), key="pb_ov_strat", help="Deselect strategies to exclude them from Overview stats", ) sel_syms = fc2.multiselect( "Filter symbols", sym_labels, default=sym_labels, key="pb_ov_sym", help="Deselect symbols to exclude them from Overview stats", ) sel_strats_raw = [strat_numbered[k] for k in sel_strat_nums] else: sel_strats_raw = strat_labels sel_syms = sym_labels ov_df = df.copy() if "close_time" in ov_df.columns and ov_date_from and ov_date_to: ct = pd.to_datetime(ov_df["close_time"]).dt.tz_localize(None) ov_df = ov_df[ (ct >= pd.Timestamp(ov_date_from)) & (ct <= pd.Timestamp(ov_date_to) + pd.Timedelta(days=1)) ] if sel_strats_raw and "_strategy" in ov_df.columns: ov_df = ov_df[ov_df["_strategy"].isin(sel_strats_raw)] if sel_syms and "symbol" in ov_df.columns: ov_df = ov_df[ov_df["symbol"].isin(sel_syms)] stats = _calc_stats(ov_df, deposit) # ── Tabs ───────────────────────────────────────────────────────────────── tab_ov, tab_tr, tab_eq, tab_st, tab_wi, tab_pf = st.tabs([ "📋 Overview", "📜 Trades", "📈 Equity Chart", "📊 Strategies", "🔧 What-If", "🗂 Portfolios", ]) # ═════════════════════════════════════════════════════════════════════════ # OVERVIEW # ═════════════════════════════════════════════════════════════════════════ with tab_ov: if not stats: st.warning("No data for selected view.") else: np_ = stats.get("net_profit", 0) nc = "pos" if np_ >= 0 else "neg" def card(label, val, sub, cls="neutral"): st.markdown( f'
{label}
' f'
{val}
' f'
{sub}
', unsafe_allow_html=True) c1,c2,c3,c4,c5 = st.columns(5) with c1: card("Total Profit", f"${np_:,.2f}", f"{stats.get('num_trades',0):,} trades", nc) with c2: card("Win Rate", f"{stats.get('win_rate',0):.2f}%", f"{stats.get('win_count',0)}W / {stats.get('loss_count',0)}L") with c3: pf = stats.get("profit_factor", 0) card("Profit Factor", f"{pf:.2f}" if pf != float("inf") else "∞", "Gross P / Gross L") with c4: card("Max Drawdown", f"${stats.get('max_dd',0):,.2f}", f"{stats.get('max_dd_pct',0):.2f}%", "neg") with c5: card("Return / DD", f"{stats.get('ret_dd_ratio',0):.2f}", "Net profit / max DD") st.markdown("
", unsafe_allow_html=True) r1,r2,r3,r4,r5 = st.columns(5) with r1: card("Yearly Avg", f"${stats.get('yearly_avg_profit',0):,.2f}", "Annual") with r2: card("Monthly Avg", f"${stats.get('monthly_avg_profit',0):,.2f}", "Per month") with r3: card("CAGR", f"{stats.get('cagr',0)*100:.2f}%", "Compound annual") with r4: at = stats.get("avg_trade",0) card("Avg Trade", f"${at:,.2f}", "Per trade", "pos" if at>=0 else "neg") with r5: card("Max Stagnation",f"{stats.get('max_stagnation_days',0)}d", "Days w/o new high") st.markdown("
", unsafe_allow_html=True) lc, rc = st.columns(2) def kv(key, val, cls=""): st.markdown( f'
{key}' f'{val}
', unsafe_allow_html=True) with lc: st.markdown('
Strategy
', unsafe_allow_html=True) kv("# Trades", f"{stats.get('num_trades',0):,}") kv("Gross Profit", f"${stats.get('gross_profit',0):,.2f}", "pos") kv("Gross Loss", f"${stats.get('gross_loss',0):,.2f}", "neg") kv("Avg Win", f"${stats.get('avg_win',0):,.2f}", "pos") kv("Avg Loss", f"${stats.get('avg_loss',0):,.2f}", "neg") kv("W/L Ratio", f"{stats.get('win_count',0)} / {stats.get('loss_count',0)}") with rc: st.markdown('
Risk
', unsafe_allow_html=True) kv("Max Consec Wins", str(stats.get("max_consec_wins",0)), "pos") kv("Max Consec Losses", str(stats.get("max_consec_losses",0)), "neg") kv("Max DD $", f"${stats.get('max_dd',0):,.2f}", "neg") kv("Max DD %", f"{stats.get('max_dd_pct',0):.2f}%", "neg") kv("Start Date", str(stats.get("start_date","—"))[:10]) kv("End Date", str(stats.get("end_date","—"))[:10]) # [1] Monthly table toggle $ / % if "monthly_table_dollar" in stats: st.markdown("
", unsafe_allow_html=True) mt_col1, mt_col2 = st.columns([3, 1]) mt_col1.markdown('
Monthly Performance
', unsafe_allow_html=True) mt_mode = mt_col2.radio("Unit", ["$", "%"], horizontal=True, key="pb_mt_mode") tbl_key = "monthly_table_dollar" if mt_mode == "$" else "monthly_table_pct" st.markdown(_monthly_html(stats[tbl_key], mt_mode), unsafe_allow_html=True) # ═════════════════════════════════════════════════════════════════════════ # TRADES # ═════════════════════════════════════════════════════════════════════════ with tab_tr: if df.empty: st.info("No trades in selected view.") else: tr_date_from, tr_date_to = _date_slider("pb_tr") fc1, fc2, fc3, fc4 = st.columns(4) all_syms = sorted(df["symbol"].dropna().unique().tolist()) if "symbol" in df.columns else [] all_types = sorted(df["type"].dropna().unique().tolist()) if "type" in df.columns else [] all_strats = sorted(df["_strategy"].dropna().unique().tolist()) if "_strategy" in df.columns else [] filt_sym = fc1.multiselect("Symbol", all_syms, default=all_syms, key="pb_ts") filt_type = fc2.multiselect("Direction", all_types, default=all_types, key="pb_tt") filt_strat = fc3.multiselect("Strategy", all_strats, default=all_strats, key="pb_tst") result_f = fc4.selectbox("Result", ["All","Wins only","Losses only"], key="pb_tr") view = df.copy() if "close_time" in view.columns and tr_date_from and tr_date_to: ct = pd.to_datetime(view["close_time"]).dt.tz_localize(None) view = view[ (ct >= pd.Timestamp(tr_date_from)) & (ct <= pd.Timestamp(tr_date_to) + pd.Timedelta(days=1)) ] if filt_sym and "symbol" in view.columns: view = view[view["symbol"].isin(filt_sym)] if filt_type and "type" in view.columns: view = view[view["type"].isin(filt_type)] if filt_strat and "_strategy" in view.columns: view = view[view["_strategy"].isin(filt_strat)] if result_f == "Wins only": view = view[view["net_profit"] > 0] elif result_f == "Losses only": view = view[view["net_profit"] <= 0] keep = [c for c in ["_strategy","symbol","type","open_time","open_price", "close_time","close_price","volume","net_profit","comment"] if c in view.columns] rename = {"_strategy":"Strategy","symbol":"Symbol","type":"Type", "open_time":"Open Time","open_price":"Open Price", "close_time":"Close Time","close_price":"Close Price", "volume":"Volume","net_profit":"P/L ($)","comment":"Comment"} ddf = view[keep].rename(columns=rename).copy() for dc in ["Open Time","Close Time"]: if dc in ddf.columns: ddf[dc] = pd.to_datetime(ddf[dc], errors="coerce").dt.strftime("%d.%m.%Y %H:%M") # [6] Format all numeric columns to 2dp num_cols = ddf.select_dtypes(include="number").columns.tolist() fmt_dict = {c: "{:.2f}" for c in num_cols} # Compact stats bar above trade list tr_stats = _calc_stats(view, deposit) if tr_stats: ts1,ts2,ts3,ts4,ts5,ts6 = st.columns(6) _np = tr_stats.get("net_profit",0) ts1.metric("Trades", f"{tr_stats.get('num_trades',0):,}") ts2.metric("Net Profit", f"${_np:,.2f}") ts3.metric("Win Rate", f"{tr_stats.get('win_rate',0):.2f}%") ts4.metric("Profit Factor", f"{tr_stats.get('profit_factor',0):.2f}" if tr_stats.get('profit_factor',0) != float('inf') else "∞") ts5.metric("Max DD", f"${tr_stats.get('max_dd',0):,.2f}") ts6.metric("Avg Trade", f"${tr_stats.get('avg_trade',0):.2f}") st.markdown("") st.caption(f"{len(ddf):,} trades · use ⛶ to expand full screen") def _hl(val): if not isinstance(val, (int,float)): return "" if val > 0: return "background-color:#1A3A26" if val < 0: return "background-color:#3A1A1A" return "" # No fixed height — Streamlit will size to content on the page # and properly fill the screen when the fullscreen icon is clicked. st.dataframe( ddf.style .format(fmt_dict) .map(_hl, subset=["P/L ($)"] if "P/L ($)" in ddf.columns else []), use_container_width=True, ) buf = io.StringIO() ddf.to_csv(buf, index=False) st.download_button("⬇️ Export CSV", buf.getvalue(), file_name=f"trades_{active_label}.csv", mime="text/csv") # ═════════════════════════════════════════════════════════════════════════ # EQUITY CHART # ═════════════════════════════════════════════════════════════════════════ with tab_eq: if df.empty or "close_time" not in df.columns: st.info("No time-series data available.") else: # [8] Line mode options: Portfolio | Individual | Portfolio + Individual ctl1, ctl2, ctl3 = st.columns([3, 2, 2]) chart_view = ctl1.radio( "Lines", ["Portfolio", "Individual strategies", "Portfolio + Individual"], horizontal=True, key="pb_cv", ) smooth_window = ctl2.slider("Smoothing", 1, 50, 1, key="pb_sm", help="Rolling-average window (trades). 1 = raw.") show_stag = ctl3.checkbox("Show stagnation band", value=True, key="pb_stag") # Date range slider date_from, date_to = _date_slider("pb_eq") # [8] Portfolio selector + strategy filter sel_strats = list(eff_dfs.keys()) if chart_view in ("Individual strategies", "Portfolio + Individual"): # Portfolio filter: which portfolio's members to show port_names = list(portfolios.keys()) if port_names: port_filter_opts = ["All strategies"] + port_names pf_sel = st.selectbox("Filter to portfolio members", port_filter_opts, key="pb_eq_pf") if pf_sel != "All strategies" and pf_sel in portfolios: default_strats = [s for s in portfolios[pf_sel] if s in eff_dfs] else: default_strats = list(eff_dfs.keys()) else: default_strats = list(eff_dfs.keys()) sel_strats = st.multiselect( "Strategies to show", list(eff_dfs.keys()), default=default_strats, key="pb_sel_strats", ) # Determine combined df for Portfolio+Individual chart_df = df if chart_view == "Portfolio + Individual" and sel_strats: members_dfs = {k: eff_dfs[k] for k in sel_strats if k in eff_dfs} chart_df = _combine(members_dfs, deposit) if members_dfs else df cv_map = { "Portfolio": "Portfolio", "Individual strategies": "Individual", "Portfolio + Individual": "Portfolio+Individual", } fig = _build_equity_chart( chart_df, deposit, eff_dfs, portfolios, active_label, chart_view=cv_map[chart_view], smooth_window=smooth_window, show_stagnation=show_stag, date_from=date_from, date_to=date_to, selected_strategies=sel_strats, ) st.plotly_chart(fig, use_container_width=True) # ═════════════════════════════════════════════════════════════════════════ # STRATEGIES TABLE [2] lot size, no CAGR [3] smoothing [4] taller chart # ═════════════════════════════════════════════════════════════════════════ with tab_st: st.markdown("##### All Strategies — Performance Summary") tdf = _strategy_table(eff_dfs, deposit, lot_overrides) if tdf.empty: st.info("No strategies loaded.") else: def _cc(val, low=0): if not isinstance(val, (int,float)): return "" return "color:#34C27A" if val > low else "color:#E05555" if val < low else "" # [6] 2dp formatting for all numeric columns num_cols_t = tdf.select_dtypes(include="number").columns.tolist() fmt_t = {c: "{:.2f}" for c in num_cols_t if c != "Trades"} fmt_t["Trades"] = "{:.0f}" styled = ( tdf.style .format(fmt_t) .map(_cc, subset=["Net Profit ($)","Avg Trade ($)"]) .map(lambda v: _cc(v, 1.0), subset=["Profit Factor"]) .map(lambda v: "color:#E05555" if isinstance(v,(int,float)) and v < 0 else "", subset=["Max DD ($)","Max DD (%)"]) ) st.dataframe(styled, use_container_width=True, hide_index=True) # [3] Smoothing slider [4] Taller chart (height=500) st.markdown("##### Equity Curves") sc_smooth = st.slider("Curve smoothing", 1, 50, 1, key="pb_st_smooth", help="Rolling-average window (trades).") sf = go.Figure() sf.update_layout( height=500, # [4] taller margin=dict(l=40, r=20, t=10, b=10), paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="#0E1117", legend=dict(orientation="h", y=1.08, font=dict(size=10)), hovermode="x unified", ) sf.update_xaxes(gridcolor="#1E2130", zeroline=False) sf.update_yaxes(gridcolor="#1E2130", zeroline=False, tickprefix="$") for i, (lbl, sdf) in enumerate(eff_dfs.items()): if "close_time" not in sdf.columns or "net_profit" not in sdf.columns: continue sdf_s = sdf.sort_values("close_time") eq = deposit + sdf_s["net_profit"].cumsum() eq_s = _smooth(eq.reset_index(drop=True), sc_smooth) sf.add_trace(go.Scatter( x=sdf_s["close_time"].values, y=eq_s, name=lbl, mode="lines", line=dict(color=COLORS[i % len(COLORS)], width=1.5), )) st.plotly_chart(sf, use_container_width=True) # ═════════════════════════════════════════════════════════════════════════ # WHAT-IF [5] lot-size scaling per strategy # ═════════════════════════════════════════════════════════════════════════ with tab_wi: st.markdown("##### What-If: Lot Size Adjustment") st.caption( "Enter a target lot size for each strategy. The tool calculates the " "multiplier automatically from the strategy's average lot size. " "Changes apply everywhere (Overview, Equity Chart, Strategies table)." ) changed = False for label in list(strategy_dfs.keys()): orig_df = strategy_dfs[label] orig_stats = _calc_stats(orig_df, deposit) orig_dd = orig_stats.get("max_dd", 0) orig_avg_lot = orig_stats.get("avg_lot", 0.0) current_mult = lot_overrides.get(label, 1.0) # Derive the currently displayed lot size from the multiplier # (avg_lot is from original unscaled data, so effective = orig * mult) current_lot = round(orig_avg_lot * current_mult, 4) if orig_avg_lot else current_mult wi_c1, wi_c2, wi_c3, wi_c4 = st.columns([3, 1, 1, 1]) wi_c1.markdown(f"**{label}**") wi_c2.markdown( f"Avg lot
" f"{orig_avg_lot:.4f}", unsafe_allow_html=True) wi_c3.markdown( f"Current ×
" f"{current_mult:.4f}", unsafe_allow_html=True) new_lot = wi_c4.number_input( "Target lot size", min_value=0.0001, max_value=9999.0, value=float(current_lot) if current_lot > 0 else float(orig_avg_lot) if orig_avg_lot else 0.01, step=0.01, format="%.4f", key=f"pb_wi_{label}", label_visibility="collapsed", help="Enter the lot size you want for this strategy", ) # Compute new multiplier from target lot / original avg lot if orig_avg_lot and orig_avg_lot > 0: new_mult = new_lot / orig_avg_lot else: new_mult = new_lot # fallback: treat as direct multiplier new_mult = round(new_mult, 6) if abs(new_mult - current_mult) > 1e-9: st.session_state.pb_lot_overrides[label] = new_mult changed = True # Before / after metrics scaled_df = _scale_df(orig_df, new_mult) scaled_stats = _calc_stats(scaled_df, deposit) s1, s2, s3, s4 = st.columns(4) s1.metric("Net Profit", f"${scaled_stats.get('net_profit',0):,.2f}", delta=f"{scaled_stats.get('net_profit',0)-orig_stats.get('net_profit',0):+.2f}") s2.metric("Max DD", f"${scaled_stats.get('max_dd',0):,.2f}", delta=f"{scaled_stats.get('max_dd',0)-orig_dd:+.2f}") s3.metric("Profit Factor", f"{scaled_stats.get('profit_factor',0):.2f}") s4.metric("Win Rate", f"{scaled_stats.get('win_rate',0):.2f}%") st.markdown("---") if changed: st.rerun() if any(v != 1.0 for v in lot_overrides.values()): if st.button("Reset all lot sizes to original", key="pb_wi_reset"): st.session_state.pb_lot_overrides = {} st.rerun() # ═════════════════════════════════════════════════════════════════════════ # PORTFOLIOS MANAGER # ═════════════════════════════════════════════════════════════════════════ with tab_pf: st.markdown("##### Custom Portfolios") st.caption("Named portfolios appear in the View dropdown. " "Lot-size overrides from the What-If tab are reflected in portfolio stats.") with st.expander("➕ Create new portfolio", expanded=not bool(portfolios)): pname = st.text_input("Portfolio name", key="pb_pname", placeholder="e.g. Gold Strategies") members = st.multiselect("Strategies to include", list(strategy_dfs.keys()), key="pb_pmembers") if st.button("Save portfolio", key="pb_psave"): if not pname.strip(): st.warning("Enter a portfolio name.") elif not members: st.warning("Select at least one strategy.") else: st.session_state.pb_portfolios[pname.strip()] = list(members) st.success(f"✅ **{pname.strip()}** saved.") st.rerun() if not portfolios: st.info("No custom portfolios yet.") else: for pname, members in list(portfolios.items()): with st.expander(f"📁 {pname} ({len(members)} strategies)"): for m in members: mult = lot_overrides.get(m, 1.0) tag = "✅" if m in strategy_dfs else "⚠️ not loaded" mult_str = f" ×{mult:.2f}" if mult != 1.0 else "" st.markdown(f"- {m} {tag}{mult_str}") new_members = st.multiselect( "Edit members", list(strategy_dfs.keys()), default=[m for m in members if m in strategy_dfs], key=f"pb_edit_{pname}", ) ec1, ec2 = st.columns(2) if ec1.button("Update", key=f"pb_upd_{pname}"): st.session_state.pb_portfolios[pname] = list(new_members) st.success("Updated.") st.rerun() if ec2.button("🗑 Delete", key=f"pb_del_{pname}"): del st.session_state.pb_portfolios[pname] st.rerun() pm_dfs = {k: eff_dfs[k] for k in members if k in eff_dfs} if pm_dfs: pdf = _combine(pm_dfs, deposit) pstat = _calc_stats(pdf, deposit) mc1,mc2,mc3,mc4 = st.columns(4) mc1.metric("Net Profit", f"${pstat.get('net_profit',0):,.2f}") mc2.metric("Win Rate", f"{pstat.get('win_rate',0):.2f}%") mc3.metric("Profit Factor", f"{pstat.get('profit_factor',0):.2f}") mc4.metric("Max DD", f"${pstat.get('max_dd',0):,.2f}")