# Modular Multi-Pipeline Discovery Architecture - Fast Implementation > **For Claude:** REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task. **Goal:** Transform discovery system into modular, multi-pipeline architecture with early signal scanners, dynamic performance tracking, and Streamlit dashboard UI. **Approach:** Implementation-first, skip tests/docs for fast experimentation. **Branch:** `feature/modular-pipeline-architecture` (no git commits during implementation) --- ## Phase 1: Core Architecture (30 min) ### Task 1: Create Scanner Registry **Files:** - Create: `tradingagents/dataflows/discovery/scanner_registry.py` **Implementation:** ```python # tradingagents/dataflows/discovery/scanner_registry.py from abc import ABC, abstractmethod from typing import Any, Dict, List, Type class BaseScanner(ABC): """Base class for all discovery scanners.""" name: str = None pipeline: str = None def __init__(self, config: Dict[str, Any]): if self.name is None: raise ValueError(f"{self.__class__.__name__} must define 'name'") if self.pipeline is None: raise ValueError(f"{self.__class__.__name__} must define 'pipeline'") self.config = config self.scanner_config = config.get("discovery", {}).get("scanners", {}).get(self.name, {}) self.enabled = self.scanner_config.get("enabled", True) self.limit = self.scanner_config.get("limit", 10) @abstractmethod def scan(self, state: Dict[str, Any]) -> List[Dict[str, Any]]: """Return list of candidates with: ticker, source, context, priority""" pass def is_enabled(self) -> bool: return self.enabled class ScannerRegistry: """Global scanner registry.""" def __init__(self): self.scanners: Dict[str, Type[BaseScanner]] = {} def register(self, scanner_class: Type[BaseScanner]): if not hasattr(scanner_class, "name") or scanner_class.name is None: raise ValueError(f"Scanner must define 'name'") if not hasattr(scanner_class, "pipeline") or scanner_class.pipeline is None: raise ValueError(f"Scanner must define 'pipeline'") self.scanners[scanner_class.name] = scanner_class def get_scanners_by_pipeline(self, pipeline: str) -> List[Type[BaseScanner]]: return [sc for sc in self.scanners.values() if sc.pipeline == pipeline] def get_all_scanners(self) -> List[Type[BaseScanner]]: return list(self.scanners.values()) SCANNER_REGISTRY = ScannerRegistry() ``` --- ### Task 2: Update Config with Modular Structure **Files:** - Modify: `tradingagents/default_config.py` **Add to config:** ```python "discovery": { # ... existing settings ... # PIPELINES: Define ranking behavior per pipeline "pipelines": { "edge": { "enabled": True, "priority": 1, "ranker_prompt": "edge_signals_ranker.txt", "deep_dive_budget": 15 }, "momentum": { "enabled": True, "priority": 2, "ranker_prompt": "momentum_ranker.txt", "deep_dive_budget": 10 }, "news": { "enabled": True, "priority": 3, "ranker_prompt": "news_catalyst_ranker.txt", "deep_dive_budget": 5 }, "social": { "enabled": True, "priority": 4, "ranker_prompt": "social_signals_ranker.txt", "deep_dive_budget": 5 }, "events": { "enabled": False, "priority": 5, "deep_dive_budget": 0 } }, # SCANNERS: Each declares its pipeline "scanners": { # Edge signals "insider_buying": {"enabled": True, "pipeline": "edge", "limit": 20}, "options_flow": {"enabled": True, "pipeline": "edge", "limit": 15}, "congress_trades": {"enabled": False, "pipeline": "edge", "limit": 10}, # Momentum "volume_accumulation": {"enabled": True, "pipeline": "momentum", "limit": 15}, "market_movers": {"enabled": True, "pipeline": "momentum", "limit": 10}, # News "semantic_news": {"enabled": True, "pipeline": "news", "limit": 10}, "analyst_upgrade": {"enabled": False, "pipeline": "news", "limit": 5}, # Social "reddit_trending": {"enabled": True, "pipeline": "social", "limit": 15}, "reddit_dd": {"enabled": True, "pipeline": "social", "limit": 10}, # Events "earnings_calendar": {"enabled": False, "pipeline": "events", "limit": 10}, "short_squeeze": {"enabled": False, "pipeline": "events", "limit": 5} } } ``` --- ## Phase 2: New Edge Scanners (45 min) ### Task 3: Insider Buying Scanner **Files:** - Create: `tradingagents/dataflows/discovery/scanners/insider_buying.py` **Implementation:** ```python # tradingagents/dataflows/discovery/scanners/insider_buying.py """SEC Form 4 insider buying scanner.""" import re from datetime import datetime, timedelta from typing import Any, Dict, List from tradingagents.dataflows.discovery.scanner_registry import BaseScanner, SCANNER_REGISTRY class InsiderBuyingScanner(BaseScanner): """Scan SEC Form 4 for insider purchases.""" name = "insider_buying" pipeline = "edge" def __init__(self, config: Dict[str, Any]): super().__init__(config) self.lookback_days = self.scanner_config.get("lookback_days", 7) def scan(self, state: Dict[str, Any]) -> List[Dict[str, Any]]: if not self.is_enabled(): return [] print(f" 💼 Scanning insider buying (last {self.lookback_days} days)...") try: # Use existing FMP API or placeholder # For MVP: Return empty or use FMP insider trades endpoint candidates = [] # TODO: Implement actual Form 4 fetching # For now, placeholder that uses FMP API if available print(f" Found {len(candidates)} insider purchases") return candidates except Exception as e: print(f" Error: {e}") return [] SCANNER_REGISTRY.register(InsiderBuyingScanner) ``` --- ### Task 4: Options Flow Scanner **Files:** - Create: `tradingagents/dataflows/discovery/scanners/options_flow.py` **Implementation:** ```python # tradingagents/dataflows/discovery/scanners/options_flow.py """Unusual options activity scanner.""" from typing import Any, Dict, List import yfinance as yf from tradingagents.dataflows.discovery.scanner_registry import BaseScanner, SCANNER_REGISTRY class OptionsFlowScanner(BaseScanner): """Scan for unusual options activity.""" name = "options_flow" pipeline = "edge" def __init__(self, config: Dict[str, Any]): super().__init__(config) self.min_volume_oi_ratio = self.scanner_config.get("min_volume_oi_ratio", 2.0) # Focus on liquid options self.ticker_universe = ["AAPL", "MSFT", "GOOGL", "AMZN", "META", "NVDA", "AMD", "TSLA"] def scan(self, state: Dict[str, Any]) -> List[Dict[str, Any]]: if not self.is_enabled(): return [] print(f" 📊 Scanning unusual options activity...") candidates = [] for ticker in self.ticker_universe[:20]: # Limit for speed try: unusual = self._analyze_ticker_options(ticker) if unusual: candidates.append(unusual) if len(candidates) >= self.limit: break except: continue print(f" Found {len(candidates)} unusual options flows") return candidates def _analyze_ticker_options(self, ticker: str) -> Dict[str, Any]: try: stock = yf.Ticker(ticker) expirations = stock.options if not expirations: return None options = stock.option_chain(expirations[0]) calls = options.calls puts = options.puts # Find unusual strikes unusual_strikes = [] for _, opt in calls.iterrows(): vol = opt.get("volume", 0) oi = opt.get("openInterest", 0) if oi > 0 and vol > 1000 and (vol / oi) >= self.min_volume_oi_ratio: unusual_strikes.append({ "type": "call", "strike": opt["strike"], "volume": vol, "oi": oi }) if not unusual_strikes: return None # Calculate P/C ratio total_call_vol = calls["volume"].sum() if not calls.empty else 0 total_put_vol = puts["volume"].sum() if not puts.empty else 0 pc_ratio = total_put_vol / total_call_vol if total_call_vol > 0 else 0 sentiment = "bullish" if pc_ratio < 0.7 else "bearish" if pc_ratio > 1.3 else "neutral" return { "ticker": ticker, "source": self.name, "context": f"Unusual options: {len(unusual_strikes)} strikes, P/C={pc_ratio:.2f} ({sentiment})", "priority": "high" if sentiment == "bullish" else "medium", "strategy": "options_flow", "put_call_ratio": round(pc_ratio, 2) } except: return None SCANNER_REGISTRY.register(OptionsFlowScanner) ``` --- ## Phase 3: Dynamic Performance Tracking (30 min) ### Task 5: Position Tracker **Files:** - Create: `tradingagents/dataflows/discovery/performance/position_tracker.py` **Implementation:** ```python # tradingagents/dataflows/discovery/performance/position_tracker.py """Dynamic position tracking with time-series data.""" import json from datetime import datetime, timedelta from pathlib import Path from typing import Any, Dict, List, Optional class PositionTracker: """Track positions with continuous price monitoring.""" def __init__(self, data_dir: str = "data"): self.data_dir = Path(data_dir) self.tracking_dir = self.data_dir / "recommendations" / "tracking" self.tracking_dir.mkdir(parents=True, exist_ok=True) def create_position(self, recommendation: Dict[str, Any]) -> Dict[str, Any]: """Create new position to track.""" ticker = recommendation["ticker"] entry_price = recommendation["entry_price"] rec_date = recommendation.get("recommendation_date", datetime.now().isoformat()) return { "ticker": ticker, "recommendation_date": rec_date, "entry_price": entry_price, "pipeline": recommendation.get("pipeline", "unknown"), "scanner": recommendation.get("scanner", "unknown"), "strategy": recommendation.get("strategy_match", "unknown"), "confidence": recommendation.get("confidence", 5), "shares": recommendation.get("shares", 0), "price_history": [{ "timestamp": rec_date, "price": entry_price, "return_pct": 0.0, "hours_held": 0, "days_held": 0 }], "metrics": { "peak_return": 0.0, "current_return": 0.0, "days_held": 0, "status": "open" } } def update_position_price(self, position: Dict[str, Any], new_price: float, timestamp: Optional[str] = None) -> Dict[str, Any]: """Update position with new price.""" if timestamp is None: timestamp = datetime.now().isoformat() entry_price = position["entry_price"] entry_time = datetime.fromisoformat(position["recommendation_date"]) current_time = datetime.fromisoformat(timestamp) return_pct = ((new_price - entry_price) / entry_price) * 100.0 time_diff = current_time - entry_time hours_held = time_diff.total_seconds() / 3600 days_held = time_diff.days position["price_history"].append({ "timestamp": timestamp, "price": new_price, "return_pct": round(return_pct, 2), "hours_held": round(hours_held, 1), "days_held": days_held }) # Update metrics position["metrics"]["current_return"] = round(return_pct, 2) position["metrics"]["current_price"] = new_price position["metrics"]["days_held"] = days_held position["metrics"]["peak_return"] = max( position["metrics"]["peak_return"], return_pct ) return position def save_position(self, position: Dict[str, Any]) -> None: """Save position to disk.""" ticker = position["ticker"] rec_date = position["recommendation_date"].split("T")[0] filename = f"{ticker}_{rec_date}.json" filepath = self.tracking_dir / filename with open(filepath, "w") as f: json.dump(position, f, indent=2) def load_all_open_positions(self) -> List[Dict[str, Any]]: """Load all open positions.""" positions = [] for filepath in self.tracking_dir.glob("*.json"): with open(filepath, "r") as f: position = json.load(f) if position["metrics"]["status"] == "open": positions.append(position) return positions ``` --- ### Task 6: Position Updater Script **Files:** - Create: `scripts/update_positions.py` **Implementation:** ```python # scripts/update_positions.py """Update all open positions with current prices.""" import yfinance as yf from datetime import datetime from tradingagents.dataflows.discovery.performance.position_tracker import PositionTracker def main(): tracker = PositionTracker() positions = tracker.load_all_open_positions() if not positions: print("No open positions") return print(f"Updating {len(positions)} positions...") # Get unique tickers tickers = list(set(p["ticker"] for p in positions)) # Fetch prices try: tickers_str = " ".join(tickers) data = yf.download(tickers_str, period="1d", progress=False) prices = {} if len(tickers) == 1: prices[tickers[0]] = float(data["Close"].iloc[-1]) else: for ticker in tickers: try: prices[ticker] = float(data["Close"][ticker].iloc[-1]) except: pass # Update each position for position in positions: ticker = position["ticker"] if ticker in prices: updated = tracker.update_position_price(position, prices[ticker]) tracker.save_position(updated) print(f" {ticker}: ${prices[ticker]:.2f} ({updated['metrics']['current_return']:+.1f}%)") print(f"✅ Updated {len(positions)} positions") except Exception as e: print(f"Error: {e}") if __name__ == "__main__": main() ``` --- ## Phase 4: Streamlit Dashboard (60 min) ### Task 7: Install Dependencies & Create Entry Point **Files:** - Update: `requirements.txt` - Create: `tradingagents/ui/dashboard.py` - Create: `tradingagents/ui/utils.py` - Create: `tradingagents/ui/pages/__init__.py` **Add to requirements.txt:** ``` streamlit>=1.40.0 plotly>=5.18.0 ``` **Dashboard entry point:** ```python # tradingagents/ui/dashboard.py """Trading Discovery Dashboard.""" import streamlit as st st.set_page_config( page_title="Trading Discovery", page_icon="🎯", layout="wide" ) from tradingagents.ui.pages import home, todays_picks, portfolio, performance, settings def main(): st.sidebar.title("🎯 Trading Discovery") page = st.sidebar.radio( "Navigation", ["Home", "Today's Picks", "Portfolio", "Performance", "Settings"] ) # Quick stats st.sidebar.markdown("---") st.sidebar.markdown("### Quick Stats") try: from tradingagents.ui.utils import load_quick_stats stats = load_quick_stats() st.sidebar.metric("Open Positions", stats.get("open_positions", 0)) st.sidebar.metric("Win Rate", f"{stats.get('win_rate_7d', 0):.1f}%") except: pass # Render page if page == "Home": home.render() elif page == "Today's Picks": todays_picks.render() elif page == "Portfolio": portfolio.render() elif page == "Performance": performance.render() elif page == "Settings": settings.render() if __name__ == "__main__": main() ``` **Utils:** ```python # tradingagents/ui/utils.py """Dashboard utilities.""" import json from datetime import datetime from pathlib import Path from typing import Any, Dict, List def load_statistics() -> Dict[str, Any]: """Load performance statistics.""" stats_file = Path("data/recommendations/statistics.json") if not stats_file.exists(): return {} with open(stats_file, "r") as f: return json.load(f) def load_recommendations(date: str = None) -> List[Dict[str, Any]]: """Load recommendations for date.""" if date is None: date = datetime.now().strftime("%Y-%m-%d") rec_file = Path(f"data/recommendations/{date}_recommendations.json") if not rec_file.exists(): return [] with open(rec_file, "r") as f: data = json.load(f) return data.get("rankings", []) def load_open_positions() -> List[Dict[str, Any]]: """Load all open positions.""" from tradingagents.dataflows.discovery.performance.position_tracker import PositionTracker tracker = PositionTracker() return tracker.load_all_open_positions() def load_quick_stats() -> Dict[str, Any]: """Load sidebar quick stats.""" stats = load_statistics() positions = load_open_positions() return { "open_positions": len(positions), "win_rate_7d": stats.get("overall_7d", {}).get("win_rate", 0) } ``` --- ### Task 8: Home Page **Files:** - Create: `tradingagents/ui/pages/home.py` ```python # tradingagents/ui/pages/home.py """Home page.""" import streamlit as st import plotly.express as px import pandas as pd from tradingagents.ui.utils import load_statistics, load_open_positions def render(): st.title("🎯 Trading Discovery Dashboard") stats = load_statistics() if not stats: st.warning("No data. Run discovery first.") return # Metrics col1, col2, col3, col4 = st.columns(4) overall_7d = stats.get("overall_7d", {}) with col1: st.metric("Win Rate (7d)", f"{overall_7d.get('win_rate', 0):.1f}%") with col2: st.metric("Open Positions", len(load_open_positions())) with col3: st.metric("Avg Return (7d)", f"{overall_7d.get('avg_return', 0):+.1f}%") with col4: by_pipeline = stats.get("by_pipeline", {}) if by_pipeline: best = max(by_pipeline.items(), key=lambda x: x[1].get("win_rate_7d", 0)) st.metric("Best Pipeline", f"{best[0].title()} ({best[1].get('win_rate_7d', 0):.0f}%)") # Pipeline chart st.subheader("📊 Pipeline Performance") if by_pipeline: data = [] for pipeline, d in by_pipeline.items(): data.append({ "Pipeline": pipeline.title(), "Win Rate": d.get("win_rate_7d", 0), "Avg Return": d.get("avg_return_7d", 0), "Count": d.get("count", 0) }) df = pd.DataFrame(data) fig = px.scatter(df, x="Win Rate", y="Avg Return", size="Count", color="Pipeline", title="Pipeline Performance") fig.add_hline(y=0, line_dash="dash") fig.add_vline(x=50, line_dash="dash") st.plotly_chart(fig, use_container_width=True) ``` --- ### Task 9: Today's Picks Page **Files:** - Create: `tradingagents/ui/pages/todays_picks.py` ```python # tradingagents/ui/pages/todays_picks.py """Today's recommendations.""" import streamlit as st from datetime import datetime from tradingagents.ui.utils import load_recommendations def render(): st.title("📋 Today's Recommendations") today = datetime.now().strftime("%Y-%m-%d") recommendations = load_recommendations(today) if not recommendations: st.warning(f"No recommendations for {today}") return # Filters col1, col2, col3 = st.columns(3) with col1: pipelines = list(set(r.get("pipeline", "unknown") for r in recommendations)) pipeline_filter = st.multiselect("Pipeline", pipelines, default=pipelines) with col2: min_confidence = st.slider("Min Confidence", 1, 10, 7) with col3: min_score = st.slider("Min Score", 0, 100, 70) # Apply filters filtered = [r for r in recommendations if r.get("pipeline", "unknown") in pipeline_filter and r.get("confidence", 0) >= min_confidence and r.get("final_score", 0) >= min_score] st.write(f"**{len(filtered)}** of **{len(recommendations)}** recommendations") # Display recommendations for i, rec in enumerate(filtered, 1): ticker = rec.get("ticker", "UNKNOWN") score = rec.get("final_score", 0) confidence = rec.get("confidence", 0) with st.expander(f"#{i} {ticker} - {rec.get('company_name', '')} (Score: {score}, Conf: {confidence}/10)"): col1, col2 = st.columns([2, 1]) with col1: st.write(f"**Pipeline:** {rec.get('pipeline', 'unknown').title()}") st.write(f"**Scanner:** {rec.get('scanner', 'unknown')}") st.write(f"**Price:** ${rec.get('current_price', 0):.2f}") st.write(f"**Thesis:** {rec.get('reason', 'N/A')}") with col2: if st.button("✅ Enter Position", key=f"enter_{ticker}"): st.info("Position entry modal (TODO)") if st.button("👀 Watch", key=f"watch_{ticker}"): st.success(f"Added {ticker} to watchlist") ``` --- ### Task 10: Portfolio Page **Files:** - Create: `tradingagents/ui/pages/portfolio.py` ```python # tradingagents/ui/pages/portfolio.py """Portfolio tracker.""" import streamlit as st import plotly.express as px import pandas as pd from datetime import datetime from tradingagents.ui.utils import load_open_positions def render(): st.title("💼 Portfolio Tracker") # Manual add form with st.expander("➕ Add Position"): col1, col2, col3, col4 = st.columns(4) with col1: ticker = st.text_input("Ticker") with col2: entry_price = st.number_input("Entry Price", min_value=0.0) with col3: shares = st.number_input("Shares", min_value=0, step=1) with col4: st.write("") # Spacing if st.button("Add"): if ticker and entry_price > 0 and shares > 0: from tradingagents.dataflows.discovery.performance.position_tracker import PositionTracker tracker = PositionTracker() pos = tracker.create_position({ "ticker": ticker.upper(), "entry_price": entry_price, "shares": shares, "recommendation_date": datetime.now().isoformat(), "pipeline": "manual", "scanner": "manual", "strategy_match": "manual", "confidence": 5 }) tracker.save_position(pos) st.success(f"Added {ticker.upper()}") st.rerun() # Load positions positions = load_open_positions() if not positions: st.info("No open positions") return # Summary total_invested = sum(p["entry_price"] * p.get("shares", 0) for p in positions) total_current = sum(p["metrics"]["current_price"] * p.get("shares", 0) for p in positions) total_pnl = total_current - total_invested total_pnl_pct = (total_pnl / total_invested * 100) if total_invested > 0 else 0 col1, col2, col3, col4 = st.columns(4) with col1: st.metric("Invested", f"${total_invested:,.0f}") with col2: st.metric("Current", f"${total_current:,.0f}") with col3: st.metric("P/L", f"${total_pnl:,.0f}", delta=f"{total_pnl_pct:+.1f}%") with col4: st.metric("Positions", len(positions)) # Table st.subheader("📊 Positions") data = [] for p in positions: pnl = (p["metrics"]["current_price"] - p["entry_price"]) * p.get("shares", 0) data.append({ "Ticker": p["ticker"], "Entry": f"${p['entry_price']:.2f}", "Current": f"${p['metrics']['current_price']:.2f}", "Shares": p.get("shares", 0), "P/L": f"${pnl:.2f}", "P/L %": f"{p['metrics']['current_return']:+.1f}%", "Days": p["metrics"]["days_held"] }) df = pd.DataFrame(data) st.dataframe(df, use_container_width=True) ``` --- ### Task 11: Performance & Settings Pages (Simplified) **Files:** - Create: `tradingagents/ui/pages/performance.py` - Create: `tradingagents/ui/pages/settings.py` ```python # tradingagents/ui/pages/performance.py """Performance analytics.""" import streamlit as st import plotly.express as px import pandas as pd from tradingagents.ui.utils import load_statistics def render(): st.title("📊 Performance Analytics") stats = load_statistics() if not stats: st.warning("No data available") return # Scanner heatmap st.subheader("🔥 Scanner Performance") by_scanner = stats.get("by_scanner", {}) if by_scanner: data = [] for scanner, d in by_scanner.items(): data.append({ "Scanner": scanner, "Win Rate": d.get("win_rate_7d", 0), "Avg Return": d.get("avg_return_7d", 0), "Count": d.get("count", 0) }) df = pd.DataFrame(data) fig = px.scatter(df, x="Win Rate", y="Avg Return", size="Count", color="Win Rate", hover_data=["Scanner"], color_continuous_scale="RdYlGn") fig.add_hline(y=0, line_dash="dash") fig.add_vline(x=50, line_dash="dash") st.plotly_chart(fig, use_container_width=True) ``` ```python # tradingagents/ui/pages/settings.py """Settings page.""" import streamlit as st from tradingagents.default_config import DEFAULT_CONFIG def render(): st.title("⚙️ Settings") st.info("Configuration UI - TODO: Implement save functionality") # Show current config config = DEFAULT_CONFIG.get("discovery", {}) st.subheader("Pipelines") pipelines = config.get("pipelines", {}) for name, cfg in pipelines.items(): with st.expander(f"{name.title()} Pipeline"): st.write(f"Enabled: {cfg.get('enabled')}") st.write(f"Priority: {cfg.get('priority')}") st.write(f"Budget: {cfg.get('deep_dive_budget')}") st.subheader("Scanners") scanners = config.get("scanners", {}) for name, cfg in scanners.items(): st.checkbox(f"{name}", value=cfg.get("enabled"), key=f"scan_{name}") ``` **Create __init__.py:** ```python # tradingagents/ui/pages/__init__.py from . import home, todays_picks, portfolio, performance, settings ``` --- ## Phase 5: Integration (15 min) ### Task 12: Update Discovery Graph **Files:** - Modify: `tradingagents/graph/discovery_graph.py` **Add to imports:** ```python from tradingagents.dataflows.discovery.scanner_registry import SCANNER_REGISTRY ``` **Replace scanner_node() method:** ```python def scanner_node(self, state: DiscoveryState) -> Dict[str, Any]: """Scan using modular registry.""" print("🔍 Scanning market for opportunities...") # Performance tracking try: self.analytics.update_performance_tracking() except Exception as e: print(f" Warning: {e}") state.setdefault("tool_logs", []) # Collect by pipeline pipeline_candidates = { "edge": [], "momentum": [], "news": [], "social": [], "events": [] } pipeline_config = self.config.get("discovery", {}).get("pipelines", {}) # Run enabled scanners for scanner_class in SCANNER_REGISTRY.get_all_scanners(): pipeline = scanner_class.pipeline if not pipeline_config.get(pipeline, {}).get("enabled", True): continue try: scanner = scanner_class(self.config) if not scanner.is_enabled(): continue state["tool_executor"] = self._execute_tool_logged candidates = scanner.scan(state) pipeline_candidates[pipeline].extend(candidates) except Exception as e: print(f" Error in {scanner_class.name}: {e}") # Merge candidates all_candidates = [] for candidates in pipeline_candidates.values(): all_candidates.extend(candidates) unique_candidates = {} self._merge_candidates_into_dict(all_candidates, unique_candidates) final = list(unique_candidates.values()) print(f" Found {len(final)} unique candidates") return { "tickers": [c["ticker"] for c in final], "candidate_metadata": final, "tool_logs": state.get("tool_logs", []), "status": "scanned" } ``` --- ## Summary & Running **What's Implemented:** 1. ✅ Modular scanner registry 2. ✅ Config with pipelines/scanners 3. ✅ 2 edge scanners (insider, options) as templates 4. ✅ Dynamic position tracker 5. ✅ Position updater script 6. ✅ Full Streamlit dashboard (5 pages) 7. ✅ Discovery graph integration **To Run:** ```bash # Install dependencies pip install streamlit plotly # Update positions (run hourly) python scripts/update_positions.py # Start dashboard streamlit run tradingagents/ui/dashboard.py # Run discovery python -m cli.main analyze # Select discovery mode ``` **Next Steps:** 1. Test discovery with new architecture 2. Add more edge scanners (congress, 13F) 3. Add tests/docs when ready 4. Tune scanner limits based on performance