feat: Streamlit dashboard with sidebar, detail, portfolio, and main app
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
93
dashboard/detail.py
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93
dashboard/detail.py
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import streamlit as st
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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import pandas as pd
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import ta
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def render_detail(symbol: str, coin_data: dict, ohlcv_df: pd.DataFrame,
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news_articles: list, social_sentiment: dict, ai_summary: str):
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symbol_short = symbol.replace("USDT", "")
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signal = coin_data.get("signal", "HOLD")
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st.header(f"{symbol_short}/USDT")
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cols = st.columns(5)
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cols[0].metric("Signal", signal)
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cols[1].metric("Score", f"{coin_data.get('composite', 0):.0f}/100")
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cols[2].metric("Technical", f"{coin_data.get('technical', 0):.0f}")
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cols[3].metric("News", f"{coin_data.get('news', 0):.0f}")
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cols[4].metric("Social / AI", f"{coin_data.get('social', 0):.0f} / {coin_data.get('ai', 0):.0f}")
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tab_chart, tab_news, tab_social, tab_ai = st.tabs(["Chart", "News", "Social", "AI Analysis"])
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with tab_chart:
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_render_chart(ohlcv_df, symbol_short)
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with tab_news:
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_render_news(news_articles)
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with tab_social:
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_render_social(social_sentiment)
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with tab_ai:
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_render_ai(ai_summary)
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def _render_chart(df: pd.DataFrame, symbol: str):
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if df is None or df.empty:
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st.info("No chart data available")
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return
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df = df.copy()
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df["rsi"] = ta.momentum.RSIIndicator(df["close"]).rsi()
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macd = ta.trend.MACD(df["close"])
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df["macd"] = macd.macd()
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df["macd_signal"] = macd.macd_signal()
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bb = ta.volatility.BollingerBands(df["close"])
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df["bb_high"] = bb.bollinger_hband()
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df["bb_low"] = bb.bollinger_lband()
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df["sma20"] = df["close"].rolling(20).mean()
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fig = make_subplots(rows=3, cols=1, shared_xaxes=True, vertical_spacing=0.05,
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row_heights=[0.6, 0.2, 0.2],
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subplot_titles=(f"{symbol} Price", "RSI", "MACD"))
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fig.add_trace(go.Candlestick(x=df["timestamp"], open=df["open"], high=df["high"],
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low=df["low"], close=df["close"], name="Price"), row=1, col=1)
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fig.add_trace(go.Scatter(x=df["timestamp"], y=df["bb_high"], line=dict(color="rgba(173,216,230,0.3)"), name="BB Upper"), row=1, col=1)
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fig.add_trace(go.Scatter(x=df["timestamp"], y=df["bb_low"], line=dict(color="rgba(173,216,230,0.3)"), fill="tonexty", name="BB Lower"), row=1, col=1)
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fig.add_trace(go.Scatter(x=df["timestamp"], y=df["sma20"], line=dict(color="orange", width=1), name="SMA20"), row=1, col=1)
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fig.add_trace(go.Scatter(x=df["timestamp"], y=df["rsi"], line=dict(color="purple"), name="RSI"), row=2, col=1)
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fig.add_hline(y=70, line_dash="dash", line_color="red", row=2, col=1)
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fig.add_hline(y=30, line_dash="dash", line_color="green", row=2, col=1)
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fig.add_trace(go.Scatter(x=df["timestamp"], y=df["macd"], line=dict(color="blue"), name="MACD"), row=3, col=1)
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fig.add_trace(go.Scatter(x=df["timestamp"], y=df["macd_signal"], line=dict(color="red"), name="Signal"), row=3, col=1)
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fig.update_layout(template="plotly_dark", height=700, showlegend=False, xaxis_rangeslider_visible=False)
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st.plotly_chart(fig, use_container_width=True)
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def _render_news(articles: list):
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if not articles:
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st.info("No recent news for this coin")
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return
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for article in articles[:10]:
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votes = article.get("sentiment_votes", {})
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pos = votes.get("positive", 0)
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neg = votes.get("negative", 0)
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if pos > neg:
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icon = ":green_circle:"
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elif neg > pos:
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icon = ":red_circle:"
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else:
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icon = ":white_circle:"
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st.markdown(f"{icon} **{article['title']}**")
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st.caption(f"Published: {article.get('published_at', 'N/A')} | +{pos} -{neg}")
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st.divider()
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def _render_social(sentiment: dict):
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if not sentiment or sentiment.get("total", 0) == 0:
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st.info("No social data available")
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return
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total = sentiment["total"]
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cols = st.columns(3)
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cols[0].metric("Positive", f"{sentiment['positive']}/{total}", f"{sentiment['positive']/total*100:.0f}%")
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cols[1].metric("Negative", f"{sentiment['negative']}/{total}", f"-{sentiment['negative']/total*100:.0f}%")
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cols[2].metric("Neutral", f"{sentiment['neutral']}/{total}")
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def _render_ai(summary: str):
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if not summary:
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st.info("AI analysis not available")
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return
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st.markdown(summary)
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70
dashboard/portfolio_view.py
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70
dashboard/portfolio_view.py
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import streamlit as st
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import plotly.graph_objects as go
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import pandas as pd
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def render_portfolio(portfolio_manager, current_prices: dict, db):
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st.header("Portfolio Simulator")
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pv = portfolio_manager.get_portfolio_value(current_prices)
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cols = st.columns(5)
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cols[0].metric("Initial Capital", f"${portfolio_manager.initial_capital:.2f}")
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cols[1].metric("Current Value", f"${pv['total_value']:.2f}")
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pnl_delta = f"{pv['pnl_pct']:+.1f}%"
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cols[2].metric("Total P&L", f"${pv['total_pnl']:+.2f}", pnl_delta)
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cols[3].metric("Win Rate", f"{pv['win_rate']:.0f}%")
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cols[4].metric("Available Cash", f"${pv['cash']:.2f}")
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st.divider()
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col_left, col_right = st.columns([3, 2])
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with col_left:
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st.subheader("Current Holdings")
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if portfolio_manager.positions:
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rows = []
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for sym, pos in portfolio_manager.positions.items():
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price = current_prices.get(sym, pos["entry_price"])
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value = pos["quantity"] * price
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pnl = value - pos["invested_usd"]
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pnl_pct = (pnl / pos["invested_usd"] * 100) if pos["invested_usd"] > 0 else 0
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rows.append({
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"Coin": sym.replace("USDT", ""),
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"Invested": f"${pos['invested_usd']:.2f}",
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"Qty": f"{pos['quantity']:.6f}",
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"Entry": f"${pos['entry_price']:.4f}",
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"Current": f"${price:.4f}",
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"P&L": f"${pnl:+.2f} ({pnl_pct:+.1f}%)",
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})
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st.dataframe(pd.DataFrame(rows), use_container_width=True, hide_index=True)
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else:
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st.info("No open positions")
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with col_right:
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st.subheader("Allocation")
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if portfolio_manager.positions:
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labels = [s.replace("USDT", "") for s in portfolio_manager.positions]
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values = [p["quantity"] * current_prices.get(s, p["entry_price"])
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for s, p in portfolio_manager.positions.items()]
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labels.append("Cash")
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values.append(pv["cash"])
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fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=0.4)])
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fig.update_layout(template="plotly_dark", height=300, margin=dict(t=20, b=20))
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st.plotly_chart(fig, use_container_width=True)
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else:
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st.info("100% Cash")
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st.divider()
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st.subheader("Trade History")
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if portfolio_manager.trades:
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trade_rows = []
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for t in reversed(portfolio_manager.trades[-20:]):
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trade_rows.append({
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"Time": t["timestamp"][:16],
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"Coin": t["coin"].replace("USDT", ""),
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"Side": t["side"],
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"Price": f"${t['price']:.4f}",
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"Amount": f"${t['amount_usd']:.2f}",
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"Reason": t["reason"],
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})
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st.dataframe(pd.DataFrame(trade_rows), use_container_width=True, hide_index=True)
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else:
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st.info("No trades yet")
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46
dashboard/sidebar.py
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46
dashboard/sidebar.py
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import streamlit as st
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import json
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from config import DEFAULT_WEIGHTS
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def render_sidebar(latest_results: dict, db):
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st.sidebar.title("Crypto Signals")
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page = st.sidebar.radio("View", ["Signals", "Portfolio"], label_visibility="collapsed")
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st.sidebar.divider()
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if latest_results:
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coins = sorted(latest_results.values(), key=lambda x: x["composite"], reverse=True)
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for coin in coins:
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signal = coin["signal"]
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color = {"BUY": "green", "HOLD": "orange", "SELL": "red"}.get(signal, "gray")
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symbol_short = coin["symbol"].replace("USDT", "")
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label = f":{color}[{signal}] **{symbol_short}** — {coin['composite']:.0f}"
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if st.sidebar.button(label, key=coin["symbol"], use_container_width=True):
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st.session_state["selected_coin"] = coin["symbol"]
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st.sidebar.divider()
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st.sidebar.subheader("Signal Weights")
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weights = _load_weights(db)
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new_weights = {}
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new_weights["technical"] = st.sidebar.slider("Technical", 0.0, 1.0, weights["technical"], 0.05)
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new_weights["news"] = st.sidebar.slider("News", 0.0, 1.0, weights["news"], 0.05)
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new_weights["social"] = st.sidebar.slider("Social", 0.0, 1.0, weights["social"], 0.05)
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new_weights["ai"] = st.sidebar.slider("AI", 0.0, 1.0, weights["ai"], 0.05)
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total = sum(new_weights.values())
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if abs(total - 1.0) > 0.01:
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st.sidebar.warning(f"Weights sum: {total:.2f} (must be 1.0)")
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else:
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if new_weights != weights:
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db.save_setting("weights", json.dumps(new_weights))
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st.session_state["weights_changed"] = True
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return page
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def _load_weights(db) -> dict:
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raw = db.load_setting("weights")
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if raw:
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try:
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return json.loads(raw)
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except json.JSONDecodeError:
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pass
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return dict(DEFAULT_WEIGHTS)
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