-
{signal_icon}
-
{signal_text}
-
Price: {latest['close']:.2f} | RSI: {rsi_val:.1f} | ATR%: {atr_val:.4f}%
+
+
{sl_label}
+
{sl_val}
+
+ """, unsafe_allow_html=True)
+ with col_tp:
+ tp_val = format_price(tp_px) if tp_px == tp_px else "โ"
+ if pos2 == 1:
+ diff_px = tp_px - entry_px
+ diff_pct = diff_px / entry_px * 100
+ tp_detail = f"+${diff_px:.2f} (+{diff_pct:.2f}%)" if diff_px == diff_px else ""
+ elif pos2 == -1:
+ diff_px = entry_px - tp_px
+ diff_pct = diff_px / entry_px * 100
+ tp_detail = f"+${diff_px:.2f} (+{diff_pct:.2f}%)" if diff_px == diff_px else ""
+ else:
+ tp_detail = ""
+ st.markdown(f"""
+
+
{tp_label}
+
{tp_val}
+
{tp_detail}
""", unsafe_allow_html=True)
else:
- st.error(f"Could not load data for {pair}.")
+ st.markdown(f"""
+
+ No active signal. Waiting for setup conditions...
+
+ """, unsafe_allow_html=True)
+# โโโ Context row โโโ
st.markdown("---")
+col_rsi, col_atr, col_vol = st.columns(3)
+with col_rsi:
+ rsi_c = "#00C853" if 40 <= rsi_val <= 60 else "#FF5252"
+ st.markdown(f"
RSI
{rsi_val:.1f}
", unsafe_allow_html=True)
+with col_atr:
+ st.markdown(f"
ATR%
{atr_val:.3f}%
", unsafe_allow_html=True)
+with col_vol:
+ vol = latest.get("volume", 0)
+ st.markdown(f"
Volume
{int(vol):,}
", unsafe_allow_html=True)
-# โโโ Equity Curve โโโ
-st.subheader("๐ฐ Equity Curve")
-
-if data is not None:
- df = data.copy()
- df["returns"] = df["close"].pct_change()
- df["strategy_returns"] = df["position"].shift(1) * df["returns"]
- df["equity"] = 10000 * (1 + df["strategy_returns"]).cumprod()
- df["buy_hold"] = 10000 * (1 + df["returns"]).cumprod()
-
- fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.05, row_heights=[0.7, 0.3])
- fig.add_trace(go.Scatter(x=df["time"], y=df["equity"], line=dict(color=COLORS["green"], width=2), name="Strategy"), row=1, col=1)
- fig.add_trace(go.Scatter(x=df["time"], y=df["buy_hold"], line=dict(color="#9E9E9E", width=1, dash="dash"), name="Buy & Hold"), row=1, col=1)
-
- peak = df["equity"].expanding().max()
- dd = (df["equity"] - peak) / peak * 100
- fig.add_trace(go.Scatter(x=df["time"], y=dd, fill="tozeroy", line=dict(color=COLORS["red"], width=1), name="Drawdown"), row=2, col=1)
-
- fig.update_layout(height=350, template="plotly_dark", hovermode="x unified",
- margin=dict(l=0, r=0, t=10, b=0), legend=dict(orientation="h", y=1.02, x=0))
- st.plotly_chart(fig, width="stretch")
-
+# โโโ Mini Price Line โโโ
st.markdown("---")
+st.markdown("
Price (last 50 candles)
", unsafe_allow_html=True)
-# โโโ Recent Signals โโโ
-st.subheader("๐ Recent Activity")
+chart_data = df[["time", "close"]].tail(50).copy()
+chart_data.columns = ["t", "price"]
+st.line_chart(chart_data.set_index("t"), height=150, color="#FFD600")
-if data is not None:
- col1, col2 = st.columns(2)
+# โโโ Recent Signal History โโโ
+if not recent_signals.empty:
+ st.markdown("---")
+ st.markdown("
Recent Signals
", unsafe_allow_html=True)
- with col1:
- sig_cols = ["time", "close", "rsi", "atr_pct", "signal"]
- if is_gold:
- sig_cols += ["sl_price", "tp_price", "exit_reason"]
- else:
- sig_cols += ["position"]
+ hist = recent_signals[["time", "close", "signal", "exit_reason"]].copy()
+ hist["time"] = hist["time"].dt.strftime("%H:%M")
+ hist["signal"] = hist["signal"].map({1: "๐ข BUY", -1: "๐ด SELL"})
+ hist = hist.rename(columns={"time": "T", "close": "Price", "signal": "Sig", "exit_reason": "Exit"})
+ hist["Exit"] = hist["Exit"].replace("", "โ")
+ st.dataframe(hist, use_container_width=True, hide_index=True, height=200)
+else:
+ st.markdown("---")
+ st.markdown("
No signals generated in recent data.
", unsafe_allow_html=True)
- recent = data[sig_cols].tail(30).copy()
- recent["signal"] = recent["signal"].map({1: "๐ข BUY", -1: "๐ด SELL", 0: "โช"})
-
- if is_gold and "exit_reason" in recent.columns:
- recent["exit_reason"] = recent["exit_reason"].replace("", "-")
- recent = recent.rename(columns={"time": "Time", "close": "Price", "rsi": "RSI",
- "atr_pct": "ATR%", "signal": "Signal",
- "sl_price": "SL", "tp_price": "TP", "exit_reason": "Exit"})
- recent["Time"] = recent["Time"].dt.strftime("%H:%M")
- recent["Price"] = recent["Price"].round(2)
- recent["SL"] = recent["SL"].round(2)
- recent["TP"] = recent["TP"].round(2)
- display_cols = ["Time", "Price", "RSI", "Signal", "SL", "TP", "Exit"]
- else:
- recent = recent.rename(columns={"time": "Time", "close": "Price", "rsi": "RSI",
- "atr_pct": "ATR%", "signal": "Signal"})
- recent["Time"] = recent["Time"].dt.strftime("%H:%M" if tf in ("1m","5m","15m","30m") else "%m/%d %H:%M")
- recent["Price"] = recent["Price"].round(5) if not is_gold else recent["Price"]
- display_cols = ["Time", "Price", "RSI", "ATR%", "Signal"]
-
- st.markdown("**Recent candles & signals**")
- st.dataframe(recent[display_cols], width="stretch", hide_index=True)
-
- with col2:
- if is_gold and not data[data["signal"] != 0].empty:
- signals = data[data["signal"] != 0].tail(20).copy()
- st.markdown("**Trade exits breakdown**")
- exit_data = signals[signals["exit_reason"] != ""].copy()
- if not exit_data.empty:
- exit_data["hold_bars"] = 0
- for i in range(len(exit_data)):
- idx = exit_data.index[i]
- prev_sig = signals[signals.index < idx]
- if not prev_sig.empty:
- entry_idx = prev_sig.index[-1]
- exit_data.loc[idx, "hold_bars"] = signals.index.get_loc(idx) - signals.index.get_loc(entry_idx)
-
- exit_data["entry_time"] = ""
- for i in range(len(exit_data)):
- idx = exit_data.index[i]
- prev = signals[signals.index < idx]
- if not prev.empty:
- exit_data.loc[idx, "entry_time"] = prev.iloc[-1]["time"]
-
- exit_display = exit_data[["time", "close", "exit_reason"]].tail(10).copy()
- exit_display["time"] = exit_display["time"].dt.strftime("%H:%M")
- exit_display = exit_display.rename(columns={"time": "Time", "close": "Price", "exit_reason": "Exit"})
- st.dataframe(exit_display, width="stretch", hide_index=True)
- else:
- st.info("No exits yet in recent data.")
- else:
- st.markdown("**Strategy metrics**")
- if perf:
- cols_left, cols_right = st.columns(2)
- perf_items = [(k, v) for k, v in perf.items() if not isinstance(v, dict)]
- mid = len(perf_items) // 2
- with cols_left:
- for k, v in perf_items[:mid]:
- st.metric(k.replace("_", " ").title(), v)
- with cols_right:
- for k, v in perf_items[mid:]:
- st.metric(k.replace("_", " ").title(), v)
-
-# Footer
+# โโโ Footer โโโ
st.markdown("---")
-st.caption("""
-**XAU Scalp Monitor** โ Data: Yahoo Finance | Strategy: Gold Scalping (5-15 min holds)
-Deployed on Streamlit Community Cloud ยท Fully automated ยท Free forever
-""")
+st.markdown("
Auto-refresh every 60s ยท Data: Yahoo Finance GC=F
", unsafe_allow_html=True)
+
+# Auto-refresh
+st.rerun(60)
diff --git a/dashboard/app_full.py b/dashboard/app_full.py
new file mode 100644
index 0000000..b9d4066
--- /dev/null
+++ b/dashboard/app_full.py
@@ -0,0 +1,412 @@
+"""
+Forex Quant Dashboard โ Streamlit App
+Monitor signals, performance, and live prices from anywhere.
+Default focus: XAU/USD Gold Scalping (5m, 5-15 min holds)
+"""
+import sys
+from pathlib import Path
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+import warnings
+warnings.filterwarnings("ignore")
+
+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.graph_objects as go
+import plotly.express as px
+from plotly.subplots import make_subplots
+from datetime import datetime, timedelta, timezone
+
+from data.fx_data import get_forex_data, AVAILABLE_PAIRS
+from strategies.xau_scalp import add_indicators_xau, generate_signals_xau, calculate_performance_xau
+
+st.set_page_config(
+ page_title="XAU Scalp Monitor",
+ page_icon="๐ฅ",
+ layout="wide",
+ initial_sidebar_state="expanded",
+)
+
+COLORS = {"bg": "#0E1117", "card": "#1A1D23", "green": "#00C853",
+ "red": "#FF1744", "blue": "#448AFF", "yellow": "#FFD600", "text": "#E0E0E0"}
+
+st.markdown("""
+
+""", unsafe_allow_html=True)
+
+# โโโ Sidebar โโโ
+st.sidebar.title("๐ฅ XAU Scalp Monitor")
+st.sidebar.markdown("---")
+
+# Build pair list with XAU/USD first (avoids import edge case on Streamlit Cloud)
+ALL_PAIRS = list(AVAILABLE_PAIRS)
+if "XAU_USD" not in ALL_PAIRS:
+ ALL_PAIRS = ["XAU_USD"] + ALL_PAIRS
+display_pairs = {p: p.replace("_", "/") for p in ALL_PAIRS}
+pair = st.sidebar.selectbox("Instrument", ALL_PAIRS, index=ALL_PAIRS.index("XAU_USD"),
+ format_func=lambda x: display_pairs.get(x, x))
+
+tf_options = {"1m": "1 Min", "5m": "5 Min", "15m": "15 Min", "30m": "30 Min",
+ "1h": "1 Hour", "4h": "4 Hour", "1d": "1 Day"}
+tf = st.sidebar.selectbox("Timeframe", list(tf_options.keys()),
+ format_func=lambda x: tf_options[x], index=1) # default 5m
+
+# Volume of data
+if tf == "1m":
+ default_days = 7
+elif tf == "5m":
+ default_days = 30
+elif tf in ("15m", "30m"):
+ default_days = 60
+else:
+ default_days = 90
+
+days_back = st.sidebar.slider("Lookback (days)", 1, 180, default_days)
+
+st.sidebar.markdown("---")
+st.sidebar.subheader("Scalping Params")
+mom_thresh = st.sidebar.slider("Mom Threshold", 0.30, 0.80, 0.55, 0.05)
+sl_mult = st.sidebar.slider("SL (ATR mult)", 0.5, 2.0, 1.2, 0.1)
+tp_mult = st.sidebar.slider("TP (ATR mult)", 1.0, 3.0, 2.0, 0.1)
+max_hold = st.sidebar.slider("Max Hold (bars)", 2, 30, 4)
+
+# Convert hold to minutes hint
+hold_minutes = max_hold * (1 if tf == "1m" else 5 if tf == "5m" else 15 if tf == "15m" else 30)
+st.sidebar.caption(f"โ {hold_minutes} min max hold")
+
+st.sidebar.markdown("---")
+st.sidebar.caption(f"Data: Yahoo Finance (free)")
+st.sidebar.caption(f"Updated: {datetime.now(timezone.utc):%Y-%m-%d %H:%M} UTC")
+auto_refresh = st.sidebar.checkbox("Auto-refresh 60s", value=False)
+
+if auto_refresh:
+ st.sidebar.info("๐ Auto-refreshing...")
+ st.rerun(60)
+
+def _fetch_commodity(pair, tf, days):
+ """Direct Yahoo fetch for commodities (bypass YAHOO_PAIRS issues on Streamlit Cloud)."""
+ import yfinance as yf
+ tickers = {"XAU_USD": "GC=F", "XAG_USD": "SI=F"}
+ yf_tf = {"1m":"1m","5m":"5m","15m":"15m","30m":"30m","1h":"60m","4h":"60m","1d":"1d"}
+ raw = yf.download(tickers[pair], period=f"{max(1,days)}d", interval=yf_tf.get(tf,"5m"), progress=False)
+ if raw is None or raw.empty:
+ return None
+ if isinstance(raw.columns, pd.MultiIndex):
+ raw.columns = raw.columns.get_level_values(0)
+ df = raw.reset_index()
+ df.columns = [c.lower().strip() for c in df.columns]
+ col_map = {"datetime":"time","dat":"time","date":"time",
+ "open":"open","high":"high","low":"low","close":"close","volume":"volume"}
+ df = df.rename(columns={k:v for k,v in col_map.items() if k in df.columns})
+ df["pair"] = pair
+ df["time"] = pd.to_datetime(df["time"])
+ return df.sort_values("time").reset_index(drop=True)
+
+# โโโ Load Data โโโ
+@st.cache_data(ttl=120)
+def load_data(pr, tf_str, days):
+ if pr in ("XAU_USD", "XAG_USD"):
+ df = _fetch_commodity(pr, tf_str, days)
+ else:
+ df = get_forex_data(pr, tf_str, years_back=max(0.01, days/365), cache=True)
+ if df is None or df.empty or len(df) < 60:
+ return None
+ if pr in ("XAU_USD", "XAG_USD"):
+ df = add_indicators_xau(df)
+ df = generate_signals_xau(df, mom_threshold=mom_thresh, atr_sl_mult=sl_mult,
+ atr_tp_mult=tp_mult, max_hold_bars=max_hold)
+ else:
+ from strategies.momentum import add_indicators, generate_signals
+ df = add_indicators(df)
+ df = generate_signals(df)
+ return df
+
+# โโโ Main Dashboard โโโ
+st.subheader("๐ฐ Live Prices")
+
+with st.spinner("Loading market data..."):
+ key_pairs = ["XAU_USD", "EUR_USD", "GBP_USD", "USD_JPY", "XAG_USD"]
+ cols = st.columns(len(key_pairs))
+ for i, p in enumerate(key_pairs):
+ try:
+ if p in ("XAU_USD", "XAG_USD"):
+ d = _fetch_commodity(p, "5m", 5)
+ else:
+ d = get_forex_data(p, "5m", 0.02, cache=True)
+ if d is not None and len(d) > 2:
+ l = d.iloc[-1]; pv = d.iloc[-2]
+ chg = (l["close"] - pv["close"]) / pv["close"] * 100
+ arrow = "โฒ" if chg >= 0 else "โผ"
+ color = COLORS["green"] if chg >= 0 else COLORS["red"]
+ label = "XAU/USD" if p == "XAU_USD" else p.replace("_", "/")
+ with cols[i]:
+ st.markdown(f"""
+
+
๐ฅ {label}
+
{l['close']:.2f}
+
{arrow} {chg:+.3f}%
+
+ """, unsafe_allow_html=True)
+ except:
+ pass
+
+st.markdown("---")
+
+# โโโ Main Chart โโโ
+is_gold = "XAU" in pair
+asset_label = "XAU/USD Gold" if is_gold else pair.replace("_", "/")
+st.subheader(f"๐ {asset_label} โ {'Scalping' if is_gold else 'Momentum'} Strategy")
+
+data = load_data(pair, tf, days_back)
+
+if data is not None:
+ col1, col2 = st.columns([2, 1])
+
+ with col1:
+ fig = make_subplots(rows=3, cols=1, shared_xaxes=True,
+ vertical_spacing=0.04, row_heights=[0.50, 0.25, 0.25],
+ subplot_titles=(f"{asset_label} Price & Signals", "MACD (Fast)", "RSI"))
+
+ # Candlestick
+ fig.add_trace(go.Candlestick(x=data["time"], open=data["open"], high=data["high"],
+ low=data["low"], close=data["close"], name="Price",
+ showlegend=False), row=1, col=1)
+
+ # Buy/Sell signals
+ buys = data[data["signal"] == 1]
+ sells = data[data["signal"] == -1]
+ if not buys.empty:
+ fig.add_trace(go.Scatter(x=buys["time"], y=buys["close"],
+ mode="markers", marker=dict(symbol="triangle-up", size=10, color=COLORS["green"]),
+ name="๐ข Buy"), row=1, col=1)
+ if not sells.empty:
+ fig.add_trace(go.Scatter(x=sells["time"], y=sells["close"],
+ mode="markers", marker=dict(symbol="triangle-down", size=10, color=COLORS["red"]),
+ name="๐ด Sell"), row=1, col=1)
+
+ # EMAs for gold, MAs for forex
+ if is_gold:
+ for col_name, label, color in [("ema_5", "EMA-5", "#00E5FF"), ("ema_8", "EMA-8", COLORS["blue"]),
+ ("ema_13", "EMA-13", COLORS["yellow"]), ("ema_21", "EMA-21", "#FF9100")]:
+ if col_name in data.columns:
+ fig.add_trace(go.Scatter(x=data["time"], y=data[col_name],
+ line=dict(color=color, width=1), name=label), row=1, col=1)
+ else:
+ for col_name, label, color in [("ma_fast", "MA-8", COLORS["blue"]), ("ma_mid", "MA-21", COLORS["yellow"])]:
+ if col_name in data.columns:
+ fig.add_trace(go.Scatter(x=data["time"], y=data[col_name],
+ line=dict(color=color, width=1), name=label), row=1, col=1)
+
+ # SL/TP lines
+ if "sl_price" in data.columns:
+ sl_data = data.dropna(subset=["sl_price"])
+ if not sl_data.empty:
+ fig.add_trace(go.Scatter(x=sl_data["time"], y=sl_data["sl_price"],
+ line=dict(color=COLORS["red"], width=0.5, dash="dot"), name="Stop Loss",
+ opacity=0.4), row=1, col=1)
+ tp_data = data.dropna(subset=["tp_price"])
+ if not tp_data.empty:
+ fig.add_trace(go.Scatter(x=tp_data["time"], y=tp_data["tp_price"],
+ line=dict(color=COLORS["green"], width=0.5, dash="dot"), name="Take Profit",
+ opacity=0.4), row=1, col=1)
+
+ # MACD
+ if "macd" in data.columns:
+ fig.add_trace(go.Bar(x=data["time"], y=data["macd_hist"],
+ marker_color=np.where(data["macd_hist"] >= 0, COLORS["green"], COLORS["red"]),
+ name="MACD Hist"), row=2, col=1)
+ fig.add_trace(go.Scatter(x=data["time"], y=data["macd"],
+ line=dict(color=COLORS["blue"], width=1.5), name="MACD"), row=2, col=1)
+ fig.add_trace(go.Scatter(x=data["time"], y=data["macd_signal"],
+ line=dict(color=COLORS["yellow"], width=1.5), name="Signal"), row=2, col=1)
+
+ # RSI
+ if "rsi" in data.columns:
+ fig.add_trace(go.Scatter(x=data["time"], y=data["rsi"],
+ line=dict(color=COLORS["blue"], width=1.5), name="RSI"), row=3, col=1)
+ fig.add_hline(y=70, line_dash="dash", line_color=COLORS["red"], row=3, col=1)
+ fig.add_hline(y=30, line_dash="dash", line_color=COLORS["green"], row=3, col=1)
+
+ fig.update_layout(height=650, template="plotly_dark", hovermode="x unified",
+ margin=dict(l=0, r=0, t=30, b=0),
+ legend=dict(orientation="h", y=1.02, x=0))
+ fig.update_xaxes(rangeslider_visible=False)
+ st.plotly_chart(fig, width="stretch")
+
+ with col2:
+ perf = calculate_performance_xau(data) if is_gold else (
+ __import__('strategies.momentum', fromlist=['calculate_performance']).calculate_performance(data))
+
+ st.markdown("### ๐ Performance")
+ metrics = [
+ ("Return", f"{perf.get('total_return_pct', 0):+.2f}%", "positive" if perf.get('total_return_pct', 0) > 0 else "negative"),
+ ("Buy & Hold", f"{perf.get('buy_hold_return_pct', 0):+.2f}%", "positive" if perf.get('buy_hold_return_pct', 0) > 0 else "negative"),
+ ("Sharpe", f"{perf.get('sharpe_ratio', 'N/A')}", "positive" if isinstance(perf.get('sharpe_ratio'), (int,float)) and perf['sharpe_ratio'] > 1 else "negative"),
+ ("Max DD", f"{perf.get('max_drawdown_pct', 0):.2f}%", "negative"),
+ ("Win Rate", f"{perf.get('win_rate_pct', 0):.1f}%", "positive" if perf.get('win_rate_pct', 50) > 50 else "negative"),
+ ]
+ if is_gold:
+ metrics += [
+ ("Trades", f"{perf.get('num_trades', 0)}", "neutral"),
+ ("Avg Hold", f"{perf.get('avg_hold_bars', 0)} bars", "neutral"),
+ ("Avg Trade", f"{perf.get('avg_trade_pct', 0):+.3f}%", "positive" if perf.get('avg_trade_pct', 0) > 0 else "negative"),
+ ("Exposure", f"{perf.get('exposure_pct', 0):.1f}%", "neutral"),
+ ]
+ else:
+ metrics += [("Trades", f"{perf.get('num_trades', 0)}", "neutral"),
+ ("Exposure", f"{perf.get('exposure_pct', 0):.1f}%", "neutral")]
+
+ for label, value, cls in metrics:
+ st.markdown(f"""
+
+ {label}
+ {value}
+
+ """, unsafe_allow_html=True)
+
+ if is_gold and "exit_reasons" in perf and perf["exit_reasons"]:
+ st.markdown("---")
+ st.markdown("### ๐ช Exit Reasons")
+ total_exits = sum(perf["exit_reasons"].values())
+ for reason, count in sorted(perf["exit_reasons"].items(), key=lambda x: -x[1]):
+ pct = count / total_exits * 100 if total_exits > 0 else 0
+ emoji = {"stop_loss": "๐ด", "take_profit": "๐ข", "timeout": "โฐ", "reversal": "๐"}.get(reason, "โช")
+ st.markdown(f"{emoji} **{reason}**: {count} ({pct:.0f}%)")
+
+ st.markdown("---")
+ latest = data.iloc[-1]
+ pos = latest.get("position", 0)
+ signal_icon = "๐ข" if pos == 1 else "๐ด" if pos == -1 else "โช"
+ signal_text = "LONG" if pos == 1 else "SHORT" if pos == -1 else "FLAT"
+ rsi_val = latest.get("rsi", 50)
+ atr_val = latest.get("atr_pct", 0)
+
+ st.markdown("### ๐ Current Status")
+ st.markdown(f"""
+
+
{signal_icon}
+
{signal_text}
+
Price: {latest['close']:.2f} | RSI: {rsi_val:.1f} | ATR%: {atr_val:.4f}%
+
+ """, unsafe_allow_html=True)
+
+else:
+ st.error(f"Could not load data for {pair}.")
+
+st.markdown("---")
+
+# โโโ Equity Curve โโโ
+st.subheader("๐ฐ Equity Curve")
+
+if data is not None:
+ df = data.copy()
+ df["returns"] = df["close"].pct_change()
+ df["strategy_returns"] = df["position"].shift(1) * df["returns"]
+ df["equity"] = 10000 * (1 + df["strategy_returns"]).cumprod()
+ df["buy_hold"] = 10000 * (1 + df["returns"]).cumprod()
+
+ fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.05, row_heights=[0.7, 0.3])
+ fig.add_trace(go.Scatter(x=df["time"], y=df["equity"], line=dict(color=COLORS["green"], width=2), name="Strategy"), row=1, col=1)
+ fig.add_trace(go.Scatter(x=df["time"], y=df["buy_hold"], line=dict(color="#9E9E9E", width=1, dash="dash"), name="Buy & Hold"), row=1, col=1)
+
+ peak = df["equity"].expanding().max()
+ dd = (df["equity"] - peak) / peak * 100
+ fig.add_trace(go.Scatter(x=df["time"], y=dd, fill="tozeroy", line=dict(color=COLORS["red"], width=1), name="Drawdown"), row=2, col=1)
+
+ fig.update_layout(height=350, template="plotly_dark", hovermode="x unified",
+ margin=dict(l=0, r=0, t=10, b=0), legend=dict(orientation="h", y=1.02, x=0))
+ st.plotly_chart(fig, width="stretch")
+
+st.markdown("---")
+
+# โโโ Recent Signals โโโ
+st.subheader("๐ Recent Activity")
+
+if data is not None:
+ col1, col2 = st.columns(2)
+
+ with col1:
+ sig_cols = ["time", "close", "rsi", "atr_pct", "signal"]
+ if is_gold:
+ sig_cols += ["sl_price", "tp_price", "exit_reason"]
+ else:
+ sig_cols += ["position"]
+
+ recent = data[sig_cols].tail(30).copy()
+ recent["signal"] = recent["signal"].map({1: "๐ข BUY", -1: "๐ด SELL", 0: "โช"})
+
+ if is_gold and "exit_reason" in recent.columns:
+ recent["exit_reason"] = recent["exit_reason"].replace("", "-")
+ recent = recent.rename(columns={"time": "Time", "close": "Price", "rsi": "RSI",
+ "atr_pct": "ATR%", "signal": "Signal",
+ "sl_price": "SL", "tp_price": "TP", "exit_reason": "Exit"})
+ recent["Time"] = recent["Time"].dt.strftime("%H:%M")
+ recent["Price"] = recent["Price"].round(2)
+ recent["SL"] = recent["SL"].round(2)
+ recent["TP"] = recent["TP"].round(2)
+ display_cols = ["Time", "Price", "RSI", "Signal", "SL", "TP", "Exit"]
+ else:
+ recent = recent.rename(columns={"time": "Time", "close": "Price", "rsi": "RSI",
+ "atr_pct": "ATR%", "signal": "Signal"})
+ recent["Time"] = recent["Time"].dt.strftime("%H:%M" if tf in ("1m","5m","15m","30m") else "%m/%d %H:%M")
+ recent["Price"] = recent["Price"].round(5) if not is_gold else recent["Price"]
+ display_cols = ["Time", "Price", "RSI", "ATR%", "Signal"]
+
+ st.markdown("**Recent candles & signals**")
+ st.dataframe(recent[display_cols], width="stretch", hide_index=True)
+
+ with col2:
+ if is_gold and not data[data["signal"] != 0].empty:
+ signals = data[data["signal"] != 0].tail(20).copy()
+ st.markdown("**Trade exits breakdown**")
+ exit_data = signals[signals["exit_reason"] != ""].copy()
+ if not exit_data.empty:
+ exit_data["hold_bars"] = 0
+ for i in range(len(exit_data)):
+ idx = exit_data.index[i]
+ prev_sig = signals[signals.index < idx]
+ if not prev_sig.empty:
+ entry_idx = prev_sig.index[-1]
+ exit_data.loc[idx, "hold_bars"] = signals.index.get_loc(idx) - signals.index.get_loc(entry_idx)
+
+ exit_data["entry_time"] = ""
+ for i in range(len(exit_data)):
+ idx = exit_data.index[i]
+ prev = signals[signals.index < idx]
+ if not prev.empty:
+ exit_data.loc[idx, "entry_time"] = prev.iloc[-1]["time"]
+
+ exit_display = exit_data[["time", "close", "exit_reason"]].tail(10).copy()
+ exit_display["time"] = exit_display["time"].dt.strftime("%H:%M")
+ exit_display = exit_display.rename(columns={"time": "Time", "close": "Price", "exit_reason": "Exit"})
+ st.dataframe(exit_display, width="stretch", hide_index=True)
+ else:
+ st.info("No exits yet in recent data.")
+ else:
+ st.markdown("**Strategy metrics**")
+ if perf:
+ cols_left, cols_right = st.columns(2)
+ perf_items = [(k, v) for k, v in perf.items() if not isinstance(v, dict)]
+ mid = len(perf_items) // 2
+ with cols_left:
+ for k, v in perf_items[:mid]:
+ st.metric(k.replace("_", " ").title(), v)
+ with cols_right:
+ for k, v in perf_items[mid:]:
+ st.metric(k.replace("_", " ").title(), v)
+
+# Footer
+st.markdown("---")
+st.caption("""
+**XAU Scalp Monitor** โ Data: Yahoo Finance | Strategy: Gold Scalping (5-15 min holds)
+Deployed on Streamlit Community Cloud ยท Fully automated ยท Free forever
+""")