Update
This commit is contained in:
Youssef Aitousarrah
parent
ea4ee9176b
commit
2376fc74a1
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@ -50,7 +50,7 @@ Analyze {ticker}'s technical setup and identify the 3-5 most relevant trading si
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1. **Call get_stock_data first** to understand recent price action (request only last 6 months)
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2. **Identify current market regime** (trending up/down/sideways/breakout setup)
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3. **Select 4-6 complementary indicators** based on regime
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4. **Call get_indicators SEPARATELY for EACH** (e.g., first call with indicator="rsi", then indicator="macd")
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4. **Call get_indicators ONCE** to get a comprehensive technical report (includes RSI, MACD, Moving Averages, Bollinger Bands, ATR, etc.)
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5. **Synthesize findings** into specific trading signals
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## OUTPUT STRUCTURE (MANDATORY)
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@ -85,8 +85,8 @@ For each signal:
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| 50 SMA | $145 | Support | Trend intact if held | Ongoing |
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## CRITICAL RULES
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- ❌ DO NOT pass multiple indicators in one call: `indicator="rsi,macd"`
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- ✅ DO call get_indicators separately: `indicator="rsi"` then `indicator="macd"`
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- ❌ DO NOT try to pass specific indicators: `indicator="rsi"` (the tool gives you everything at once)
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- ✅ DO call `get_indicators(symbol=ticker, curr_date=current_date)` once to get all data
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- ❌ DO NOT say "trends are mixed" without specific examples
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- ✅ DO provide concrete signals with specific price levels and timeframes
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- ❌ DO NOT select redundant indicators (e.g., both close_50_sma and close_200_sma)
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@ -686,7 +686,7 @@ Lesson: {lesson_text}
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try:
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# Get technical/price data (what Market Analyst sees)
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stock_data = execute_tool("get_stock_data", symbol=ticker, start_date=date)
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indicators = execute_tool("get_indicators", symbol=ticker, start_date=date)
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indicators = execute_tool("get_indicators", symbol=ticker, curr_date=date)
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data["market_report"] = f"Stock Data:\n{stock_data}\n\nTechnical Indicators:\n{indicators}"
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except Exception as e:
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data["market_report"] = f"Error fetching market data: {e}"
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@ -2,7 +2,7 @@ from typing import Annotated
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# Import from vendor-specific modules
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from .local import get_YFin_data, get_finnhub_news, get_finnhub_company_insider_sentiment, get_finnhub_company_insider_transactions, get_simfin_balance_sheet, get_simfin_cashflow, get_simfin_income_statements, get_reddit_global_news, get_reddit_company_news
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from .y_finance import get_YFin_data_online, get_stock_stats_indicators_window, get_balance_sheet as get_yfinance_balance_sheet, get_cashflow as get_yfinance_cashflow, get_income_statement as get_yfinance_income_statement, get_insider_transactions as get_yfinance_insider_transactions, validate_ticker as validate_ticker_yfinance
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from .y_finance import get_YFin_data_online, get_stock_stats_indicators_window, get_technical_analysis, get_balance_sheet as get_yfinance_balance_sheet, get_cashflow as get_yfinance_cashflow, get_income_statement as get_yfinance_income_statement, get_insider_transactions as get_yfinance_insider_transactions, validate_ticker as validate_ticker_yfinance
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from .google import get_google_news, get_global_news_google
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from .openai import get_stock_news_openai, get_global_news_openai, get_fundamentals_openai
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from .alpha_vantage import (
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@ -294,6 +294,415 @@ def get_stockstats_indicator(
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return str(indicator_value)
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def get_technical_analysis(
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symbol: Annotated[str, "ticker symbol of the company"],
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curr_date: Annotated[str, "The current trading date, YYYY-mm-dd"],
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) -> str:
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"""
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Get a concise technical analysis summary with key indicators, signals, and trend interpretation.
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Returns analysis-ready output instead of verbose day-by-day data.
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"""
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from .config import get_config
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from stockstats import wrap
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# Default indicators to analyze
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indicators = ["rsi", "stoch", "macd", "adx", "close_20_ema", "close_50_sma", "close_200_sma", "boll", "atr", "obv", "vwap", "fib"]
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# Fetch price data (last 60 days for indicator calculation)
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curr_date_dt = pd.to_datetime(curr_date)
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start_date = curr_date_dt - pd.DateOffset(days=200) # Need enough history for 200 SMA
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try:
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data = yf.download(
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symbol,
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start=start_date.strftime("%Y-%m-%d"),
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end=curr_date_dt.strftime("%Y-%m-%d"),
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multi_level_index=False,
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progress=False,
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auto_adjust=True,
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)
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if data.empty:
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return f"No data found for {symbol}"
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data = data.reset_index()
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df = wrap(data)
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# Get latest values
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latest = df.iloc[-1]
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prev = df.iloc[-2] if len(df) > 1 else latest
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prev_5 = df.iloc[-5] if len(df) > 5 else latest
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current_price = float(latest['close'])
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# Build analysis
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analysis = []
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analysis.append(f"# Technical Analysis for {symbol.upper()}")
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analysis.append(f"**Date:** {curr_date}")
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analysis.append(f"**Current Price:** ${current_price:.2f}")
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analysis.append("")
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# Price action summary
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daily_change = ((current_price - float(prev['close'])) / float(prev['close'])) * 100
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weekly_change = ((current_price - float(prev_5['close'])) / float(prev_5['close'])) * 100
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analysis.append(f"## Price Action")
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analysis.append(f"- **Daily Change:** {daily_change:+.2f}%")
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analysis.append(f"- **5-Day Change:** {weekly_change:+.2f}%")
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analysis.append("")
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# RSI Analysis
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if 'rsi' in indicators:
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try:
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df['rsi'] # Trigger calculation
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rsi = float(df.iloc[-1]['rsi'])
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rsi_prev = float(df.iloc[-5]['rsi']) if len(df) > 5 else rsi
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if rsi > 70:
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rsi_signal = "OVERBOUGHT ⚠️"
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elif rsi < 30:
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rsi_signal = "OVERSOLD ⚡"
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elif rsi > 50:
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rsi_signal = "Bullish"
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else:
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rsi_signal = "Bearish"
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rsi_trend = "↑" if rsi > rsi_prev else "↓"
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analysis.append(f"## RSI (14)")
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analysis.append(f"- **Value:** {rsi:.1f} {rsi_trend}")
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analysis.append(f"- **Signal:** {rsi_signal}")
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analysis.append("")
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except Exception as e:
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pass
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# MACD Analysis
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if 'macd' in indicators:
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try:
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df['macd']
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df['macds']
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df['macdh']
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macd = float(df.iloc[-1]['macd'])
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signal = float(df.iloc[-1]['macds'])
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histogram = float(df.iloc[-1]['macdh'])
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hist_prev = float(df.iloc[-2]['macdh']) if len(df) > 1 else histogram
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if macd > signal and histogram > 0:
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macd_signal = "BULLISH CROSSOVER ⚡" if histogram > hist_prev else "Bullish"
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elif macd < signal and histogram < 0:
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macd_signal = "BEARISH CROSSOVER ⚠️" if histogram < hist_prev else "Bearish"
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else:
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macd_signal = "Neutral"
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momentum = "Strengthening ↑" if abs(histogram) > abs(hist_prev) else "Weakening ↓"
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analysis.append(f"## MACD")
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analysis.append(f"- **MACD Line:** {macd:.3f}")
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analysis.append(f"- **Signal Line:** {signal:.3f}")
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analysis.append(f"- **Histogram:** {histogram:.3f} ({momentum})")
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analysis.append(f"- **Signal:** {macd_signal}")
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analysis.append("")
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except Exception as e:
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pass
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# Moving Averages
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if 'close_50_sma' in indicators or 'close_200_sma' in indicators:
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try:
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df['close_50_sma']
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df['close_200_sma']
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sma_50 = float(df.iloc[-1]['close_50_sma'])
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sma_200 = float(df.iloc[-1]['close_200_sma'])
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# Trend determination
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if current_price > sma_50 > sma_200:
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trend = "STRONG UPTREND ⚡"
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elif current_price > sma_50:
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trend = "Uptrend"
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elif current_price < sma_50 < sma_200:
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trend = "STRONG DOWNTREND ⚠️"
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elif current_price < sma_50:
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trend = "Downtrend"
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else:
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trend = "Sideways"
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# Golden/Death cross detection
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sma_50_prev = float(df.iloc[-5]['close_50_sma']) if len(df) > 5 else sma_50
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sma_200_prev = float(df.iloc[-5]['close_200_sma']) if len(df) > 5 else sma_200
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cross = ""
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if sma_50 > sma_200 and sma_50_prev < sma_200_prev:
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cross = " (GOLDEN CROSS ⚡)"
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elif sma_50 < sma_200 and sma_50_prev > sma_200_prev:
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cross = " (DEATH CROSS ⚠️)"
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analysis.append(f"## Moving Averages")
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analysis.append(f"- **50 SMA:** ${sma_50:.2f} ({'+' if current_price > sma_50 else ''}{((current_price - sma_50) / sma_50 * 100):.1f}% from price)")
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analysis.append(f"- **200 SMA:** ${sma_200:.2f} ({'+' if current_price > sma_200 else ''}{((current_price - sma_200) / sma_200 * 100):.1f}% from price)")
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analysis.append(f"- **Trend:** {trend}{cross}")
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analysis.append("")
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except Exception as e:
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pass
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# Bollinger Bands
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if 'boll' in indicators:
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try:
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df['boll']
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df['boll_ub']
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df['boll_lb']
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middle = float(df.iloc[-1]['boll'])
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upper = float(df.iloc[-1]['boll_ub'])
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lower = float(df.iloc[-1]['boll_lb'])
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# Position within bands (0 = lower, 1 = upper)
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band_position = (current_price - lower) / (upper - lower) if upper != lower else 0.5
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if band_position > 0.95:
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bb_signal = "AT UPPER BAND - Potential reversal ⚠️"
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elif band_position < 0.05:
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bb_signal = "AT LOWER BAND - Potential bounce ⚡"
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elif band_position > 0.8:
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bb_signal = "Near upper band"
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elif band_position < 0.2:
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bb_signal = "Near lower band"
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else:
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bb_signal = "Within bands"
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bandwidth = ((upper - lower) / middle) * 100
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analysis.append(f"## Bollinger Bands (20,2)")
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analysis.append(f"- **Upper:** ${upper:.2f}")
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analysis.append(f"- **Middle:** ${middle:.2f}")
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analysis.append(f"- **Lower:** ${lower:.2f}")
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analysis.append(f"- **Band Position:** {band_position:.0%}")
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analysis.append(f"- **Bandwidth:** {bandwidth:.1f}% (volatility indicator)")
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analysis.append(f"- **Signal:** {bb_signal}")
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analysis.append("")
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except Exception as e:
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pass
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# ATR (Volatility)
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if 'atr' in indicators:
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try:
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df['atr']
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atr = float(df.iloc[-1]['atr'])
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atr_pct = (atr / current_price) * 100
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if atr_pct > 5:
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vol_level = "HIGH VOLATILITY ⚠️"
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elif atr_pct > 2:
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vol_level = "Moderate volatility"
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else:
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vol_level = "Low volatility"
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analysis.append(f"## ATR (Volatility)")
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analysis.append(f"- **ATR:** ${atr:.2f} ({atr_pct:.1f}% of price)")
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analysis.append(f"- **Level:** {vol_level}")
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analysis.append(f"- **Suggested Stop-Loss:** ${current_price - (1.5 * atr):.2f} (1.5x ATR)")
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analysis.append("")
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except Exception as e:
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pass
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# Stochastic Oscillator
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if 'stoch' in indicators:
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try:
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df['kdjk'] # Stochastic %K
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df['kdjd'] # Stochastic %D
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stoch_k = float(df.iloc[-1]['kdjk'])
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stoch_d = float(df.iloc[-1]['kdjd'])
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stoch_k_prev = float(df.iloc[-2]['kdjk']) if len(df) > 1 else stoch_k
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if stoch_k > 80 and stoch_d > 80:
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stoch_signal = "OVERBOUGHT ⚠️"
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elif stoch_k < 20 and stoch_d < 20:
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stoch_signal = "OVERSOLD ⚡"
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elif stoch_k > stoch_d and stoch_k_prev < stoch_d:
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stoch_signal = "Bullish crossover ⚡"
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elif stoch_k < stoch_d and stoch_k_prev > stoch_d:
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stoch_signal = "Bearish crossover ⚠️"
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elif stoch_k > 50:
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stoch_signal = "Bullish"
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else:
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stoch_signal = "Bearish"
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analysis.append(f"## Stochastic (14,3,3)")
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analysis.append(f"- **%K:** {stoch_k:.1f}")
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analysis.append(f"- **%D:** {stoch_d:.1f}")
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analysis.append(f"- **Signal:** {stoch_signal}")
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analysis.append("")
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except Exception as e:
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pass
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# ADX (Trend Strength)
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if 'adx' in indicators:
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try:
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df['adx']
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df['dx']
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adx = float(df.iloc[-1]['adx'])
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adx_prev = float(df.iloc[-5]['adx']) if len(df) > 5 else adx
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if adx > 50:
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trend_strength = "VERY STRONG TREND ⚡"
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elif adx > 25:
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trend_strength = "Strong trend"
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elif adx > 20:
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trend_strength = "Trending"
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else:
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trend_strength = "WEAK/NO TREND (range-bound) ⚠️"
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adx_direction = "Strengthening ↑" if adx > adx_prev else "Weakening ↓"
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analysis.append(f"## ADX (Trend Strength)")
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analysis.append(f"- **ADX:** {adx:.1f} ({adx_direction})")
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analysis.append(f"- **Interpretation:** {trend_strength}")
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analysis.append("")
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except Exception as e:
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pass
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# 20 EMA (Short-term trend)
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if 'close_20_ema' in indicators:
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try:
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df['close_20_ema']
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ema_20 = float(df.iloc[-1]['close_20_ema'])
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pct_from_ema = ((current_price - ema_20) / ema_20) * 100
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if current_price > ema_20:
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ema_signal = "Price ABOVE 20 EMA (short-term bullish)"
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else:
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ema_signal = "Price BELOW 20 EMA (short-term bearish)"
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analysis.append(f"## 20 EMA")
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analysis.append(f"- **Value:** ${ema_20:.2f} ({pct_from_ema:+.1f}% from price)")
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analysis.append(f"- **Signal:** {ema_signal}")
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analysis.append("")
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except Exception as e:
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pass
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# OBV (On-Balance Volume)
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if 'obv' in indicators:
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try:
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# Calculate OBV manually since stockstats may not have it
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obv = 0
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obv_values = [0]
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for i in range(1, len(df)):
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if float(df.iloc[i]['close']) > float(df.iloc[i-1]['close']):
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obv += float(df.iloc[i]['volume'])
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elif float(df.iloc[i]['close']) < float(df.iloc[i-1]['close']):
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obv -= float(df.iloc[i]['volume'])
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obv_values.append(obv)
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current_obv = obv_values[-1]
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obv_5_ago = obv_values[-5] if len(obv_values) > 5 else obv_values[0]
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if current_obv > obv_5_ago and current_price > float(df.iloc[-5]['close']):
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obv_signal = "Confirmed uptrend (price & volume rising)"
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elif current_obv < obv_5_ago and current_price < float(df.iloc[-5]['close']):
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obv_signal = "Confirmed downtrend (price & volume falling)"
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elif current_obv > obv_5_ago and current_price < float(df.iloc[-5]['close']):
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obv_signal = "BULLISH DIVERGENCE ⚡ (accumulation)"
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elif current_obv < obv_5_ago and current_price > float(df.iloc[-5]['close']):
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obv_signal = "BEARISH DIVERGENCE ⚠️ (distribution)"
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else:
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obv_signal = "Neutral"
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obv_formatted = f"{current_obv/1e6:.1f}M" if abs(current_obv) > 1e6 else f"{current_obv/1e3:.1f}K"
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analysis.append(f"## OBV (On-Balance Volume)")
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analysis.append(f"- **Value:** {obv_formatted}")
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analysis.append(f"- **5-Day Trend:** {'Rising ↑' if current_obv > obv_5_ago else 'Falling ↓'}")
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analysis.append(f"- **Signal:** {obv_signal}")
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analysis.append("")
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except Exception as e:
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pass
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# VWAP (Volume Weighted Average Price)
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if 'vwap' in indicators:
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try:
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# Calculate VWAP for today (simplified - using recent data)
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typical_price = (float(df.iloc[-1]['high']) + float(df.iloc[-1]['low']) + float(df.iloc[-1]['close'])) / 3
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# Calculate cumulative VWAP (last 20 periods approximation)
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recent_df = df.tail(20)
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tp_vol = ((recent_df['high'] + recent_df['low'] + recent_df['close']) / 3) * recent_df['volume']
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vwap = float(tp_vol.sum() / recent_df['volume'].sum())
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pct_from_vwap = ((current_price - vwap) / vwap) * 100
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if current_price > vwap:
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vwap_signal = "Price ABOVE VWAP (institutional buying)"
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else:
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vwap_signal = "Price BELOW VWAP (institutional selling)"
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analysis.append(f"## VWAP (20-period)")
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analysis.append(f"- **VWAP:** ${vwap:.2f}")
|
||||
analysis.append(f"- **Current vs VWAP:** {pct_from_vwap:+.1f}%")
|
||||
analysis.append(f"- **Signal:** {vwap_signal}")
|
||||
analysis.append("")
|
||||
except Exception as e:
|
||||
pass
|
||||
|
||||
# Fibonacci Retracement Levels
|
||||
if 'fib' in indicators:
|
||||
try:
|
||||
# Get high and low from last 50 periods
|
||||
recent_high = float(df.tail(50)['high'].max())
|
||||
recent_low = float(df.tail(50)['low'].min())
|
||||
diff = recent_high - recent_low
|
||||
|
||||
fib_levels = {
|
||||
"0.0% (High)": recent_high,
|
||||
"23.6%": recent_high - (diff * 0.236),
|
||||
"38.2%": recent_high - (diff * 0.382),
|
||||
"50.0%": recent_high - (diff * 0.5),
|
||||
"61.8%": recent_high - (diff * 0.618),
|
||||
"78.6%": recent_high - (diff * 0.786),
|
||||
"100% (Low)": recent_low,
|
||||
}
|
||||
|
||||
# Find nearest support and resistance
|
||||
support = None
|
||||
resistance = None
|
||||
for level_name, level_price in fib_levels.items():
|
||||
if level_price < current_price and (support is None or level_price > support[1]):
|
||||
support = (level_name, level_price)
|
||||
if level_price > current_price and (resistance is None or level_price < resistance[1]):
|
||||
resistance = (level_name, level_price)
|
||||
|
||||
analysis.append(f"## Fibonacci Levels (50-period)")
|
||||
analysis.append(f"- **Recent High:** ${recent_high:.2f}")
|
||||
analysis.append(f"- **Recent Low:** ${recent_low:.2f}")
|
||||
if resistance:
|
||||
analysis.append(f"- **Next Resistance:** ${resistance[1]:.2f} ({resistance[0]})")
|
||||
if support:
|
||||
analysis.append(f"- **Next Support:** ${support[1]:.2f} ({support[0]})")
|
||||
analysis.append("")
|
||||
except Exception as e:
|
||||
pass
|
||||
|
||||
# Overall Summary
|
||||
analysis.append("## Summary")
|
||||
signals = []
|
||||
|
||||
# Collect all signals for summary
|
||||
try:
|
||||
rsi = float(df.iloc[-1]['rsi'])
|
||||
if rsi > 70:
|
||||
signals.append("RSI overbought")
|
||||
elif rsi < 30:
|
||||
signals.append("RSI oversold")
|
||||
except:
|
||||
pass
|
||||
|
||||
try:
|
||||
if current_price > float(df.iloc[-1]['close_50_sma']):
|
||||
signals.append("Above 50 SMA")
|
||||
else:
|
||||
signals.append("Below 50 SMA")
|
||||
except:
|
||||
pass
|
||||
|
||||
if signals:
|
||||
analysis.append(f"- **Key Signals:** {', '.join(signals)}")
|
||||
|
||||
return "\n".join(analysis)
|
||||
|
||||
except Exception as e:
|
||||
return f"Error analyzing {symbol}: {str(e)}"
|
||||
|
||||
|
||||
def get_balance_sheet(
|
||||
ticker: Annotated[str, "ticker symbol of the company"],
|
||||
freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly",
|
||||
|
|
@ -388,20 +797,73 @@ def get_insider_transactions(
|
|||
ticker: Annotated[str, "ticker symbol of the company"],
|
||||
curr_date: Annotated[str, "current date (not used for yfinance)"] = None
|
||||
):
|
||||
"""Get insider transactions data from yfinance."""
|
||||
"""Get insider transactions data from yfinance with parsed transaction types."""
|
||||
try:
|
||||
ticker_obj = yf.Ticker(ticker.upper())
|
||||
data = ticker_obj.insider_transactions
|
||||
|
||||
if data is None or data.empty:
|
||||
return f"No insider transactions data found for symbol '{ticker}'"
|
||||
|
||||
# Convert to CSV string for consistency with other functions
|
||||
csv_string = data.to_csv()
|
||||
|
||||
# Add header information
|
||||
header = f"# Insider Transactions data for {ticker.upper()}\n"
|
||||
# Parse the Text column to populate Transaction type
|
||||
def classify_transaction(text):
|
||||
if pd.isna(text) or text == '':
|
||||
return 'Unknown'
|
||||
text_lower = str(text).lower()
|
||||
if 'sale' in text_lower:
|
||||
return 'Sale'
|
||||
elif 'purchase' in text_lower or 'buy' in text_lower:
|
||||
return 'Purchase'
|
||||
elif 'gift' in text_lower:
|
||||
return 'Gift'
|
||||
elif 'exercise' in text_lower or 'option' in text_lower:
|
||||
return 'Option Exercise'
|
||||
elif 'award' in text_lower or 'grant' in text_lower:
|
||||
return 'Award/Grant'
|
||||
elif 'conversion' in text_lower:
|
||||
return 'Conversion'
|
||||
else:
|
||||
return 'Other'
|
||||
|
||||
# Apply classification
|
||||
data['Transaction'] = data['Text'].apply(classify_transaction)
|
||||
|
||||
# Calculate summary statistics
|
||||
transaction_counts = data['Transaction'].value_counts().to_dict()
|
||||
total_sales_value = data[data['Transaction'] == 'Sale']['Value'].sum()
|
||||
total_purchases_value = data[data['Transaction'] == 'Purchase']['Value'].sum()
|
||||
|
||||
# Determine insider sentiment
|
||||
sales_count = transaction_counts.get('Sale', 0)
|
||||
purchases_count = transaction_counts.get('Purchase', 0)
|
||||
|
||||
if purchases_count > sales_count:
|
||||
sentiment = "BULLISH ⚡ (more buying than selling)"
|
||||
elif sales_count > purchases_count * 2:
|
||||
sentiment = "BEARISH ⚠️ (significant insider selling)"
|
||||
elif sales_count > purchases_count:
|
||||
sentiment = "Slightly bearish (more selling than buying)"
|
||||
else:
|
||||
sentiment = "Neutral"
|
||||
|
||||
# Build summary header
|
||||
header = f"# Insider Transactions for {ticker.upper()}\n"
|
||||
header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
|
||||
header += "## Summary\n"
|
||||
header += f"- **Insider Sentiment:** {sentiment}\n"
|
||||
for tx_type, count in sorted(transaction_counts.items(), key=lambda x: -x[1]):
|
||||
header += f"- **{tx_type}:** {count} transactions\n"
|
||||
if total_sales_value > 0:
|
||||
header += f"- **Total Sales Value:** ${total_sales_value:,.0f}\n"
|
||||
if total_purchases_value > 0:
|
||||
header += f"- **Total Purchases Value:** ${total_purchases_value:,.0f}\n"
|
||||
header += "\n## Transaction Details\n\n"
|
||||
|
||||
# Select key columns for output
|
||||
output_cols = ['Start Date', 'Insider', 'Position', 'Transaction', 'Shares', 'Value', 'Ownership']
|
||||
available_cols = [c for c in output_cols if c in data.columns]
|
||||
|
||||
csv_string = data[available_cols].to_csv(index=False)
|
||||
|
||||
return header + csv_string
|
||||
|
||||
|
|
@ -414,24 +876,17 @@ def validate_ticker(symbol: str) -> bool:
|
|||
"""
|
||||
try:
|
||||
ticker = yf.Ticker(symbol.upper())
|
||||
# Try to fetch 1 day of history
|
||||
# Suppress yfinance error output
|
||||
import sys
|
||||
from io import StringIO
|
||||
|
||||
# Redirect stderr to suppress yfinance error messages
|
||||
original_stderr = sys.stderr
|
||||
sys.stderr = StringIO()
|
||||
|
||||
try:
|
||||
history = ticker.history(period="1d")
|
||||
return not history.empty
|
||||
finally:
|
||||
# Restore stderr
|
||||
sys.stderr = original_stderr
|
||||
# Use fast_info for lighter validation (no historical download needed)
|
||||
# fast_info attributes are lazy-loaded
|
||||
_ = ticker.fast_info.get("lastPrice")
|
||||
return True
|
||||
|
||||
except Exception:
|
||||
return False
|
||||
# Fallback to older method if fast_info fails or is missing
|
||||
try:
|
||||
return not ticker.history(period="1d", progress=False).empty
|
||||
except:
|
||||
return False
|
||||
|
||||
|
||||
def get_fundamentals(
|
||||
|
|
|
|||
|
|
@ -202,9 +202,12 @@ Return a JSON object with a 'candidates' array of objects, each having 'ticker'
|
|||
for c in reddit_candidates:
|
||||
ticker = c.get("ticker", "").upper().strip()
|
||||
context = c.get("context", "Trending on Reddit")
|
||||
# Validate ticker format (1-5 uppercase letters)
|
||||
# Validate ticker - Exclude garbage, verify existence
|
||||
if re.match(r'^[A-Z]{1,5}$', ticker):
|
||||
candidates.append({"ticker": ticker, "source": "social_trending", "context": context, "sentiment": "unknown"})
|
||||
try:
|
||||
if execute_tool("validate_ticker", symbol=ticker):
|
||||
candidates.append({"ticker": ticker, "source": "social_trending", "context": context})
|
||||
except: pass
|
||||
except Exception as e:
|
||||
print(f" Error fetching Reddit tickers: {e}")
|
||||
|
||||
|
|
@ -247,14 +250,16 @@ Return a JSON object with a 'movers' array containing objects with 'ticker', 'ty
|
|||
for m in movers:
|
||||
ticker = m.get('ticker', '').upper().strip()
|
||||
if ticker and re.match(r'^[A-Z]{1,5}$', ticker):
|
||||
mover_type = m.get('type', 'gainer')
|
||||
reason = m.get('reason', f"Top {mover_type}")
|
||||
candidates.append({
|
||||
"ticker": ticker,
|
||||
"source": mover_type,
|
||||
"context": reason,
|
||||
"sentiment": "negative" if mover_type == "loser" else "positive"
|
||||
})
|
||||
try:
|
||||
if execute_tool("validate_ticker", symbol=ticker):
|
||||
mover_type = m.get('type', 'gainer')
|
||||
reason = m.get('reason', f"Top {mover_type}")
|
||||
candidates.append({
|
||||
"ticker": ticker,
|
||||
"source": "market_mover",
|
||||
"context": f"{reason} ({m.get('change_percent', 0)}%)"
|
||||
})
|
||||
except: pass
|
||||
|
||||
except Exception as e:
|
||||
print(f" Error fetching Market Movers: {e}")
|
||||
|
|
@ -291,7 +296,10 @@ Return a JSON object with a 'candidates' array of objects, each having 'ticker'
|
|||
ticker = c.get("ticker", "").upper().strip()
|
||||
context = c.get("context", "Upcoming earnings")
|
||||
if re.match(r'^[A-Z]{1,5}$', ticker):
|
||||
candidates.append({"ticker": ticker, "source": "earnings_catalyst", "context": context, "sentiment": "unknown"})
|
||||
try:
|
||||
if execute_tool("validate_ticker", symbol=ticker):
|
||||
candidates.append({"ticker": ticker, "source": "earnings_catalyst", "context": context})
|
||||
except: pass
|
||||
except Exception as e:
|
||||
print(f" Error fetching Earnings Calendar: {e}")
|
||||
|
||||
|
|
@ -327,7 +335,10 @@ Return a JSON object with a 'candidates' array of objects, each having 'ticker'
|
|||
ticker = c.get("ticker", "").upper().strip()
|
||||
context = c.get("context", "Recent/upcoming IPO")
|
||||
if re.match(r'^[A-Z]{1,5}$', ticker):
|
||||
candidates.append({"ticker": ticker, "source": "ipo_listing", "context": context, "sentiment": "unknown"})
|
||||
try:
|
||||
if execute_tool("validate_ticker", symbol=ticker):
|
||||
candidates.append({"ticker": ticker, "source": "ipo_listing", "context": context})
|
||||
except: pass
|
||||
except Exception as e:
|
||||
print(f" Error fetching IPO Calendar: {e}")
|
||||
|
||||
|
|
@ -364,12 +375,101 @@ Return a JSON object with a 'candidates' array of objects, each having 'ticker'
|
|||
ticker = c.get("ticker", "").upper().strip()
|
||||
context = c.get("context", "High short interest")
|
||||
if re.match(r'^[A-Z]{1,5}$', ticker):
|
||||
candidates.append({"ticker": ticker, "source": "short_squeeze", "context": context, "sentiment": "unknown"})
|
||||
try:
|
||||
if execute_tool("validate_ticker", symbol=ticker):
|
||||
candidates.append({"ticker": ticker, "source": "short_squeeze", "context": context})
|
||||
except: pass
|
||||
|
||||
print(f" Found {len(short_candidates)} short squeeze candidates")
|
||||
except Exception as e:
|
||||
print(f" Error fetching Short Interest: {e}")
|
||||
|
||||
# 6. Unusual Volume Detection (Accumulation Signal)
|
||||
try:
|
||||
from datetime import datetime
|
||||
today = datetime.now().strftime("%Y-%m-%d")
|
||||
|
||||
volume_report = execute_tool(
|
||||
"get_unusual_volume",
|
||||
date=today,
|
||||
min_volume_multiple=3.0, # 3x average volume
|
||||
max_price_change=5.0, # Less than 5% price change
|
||||
top_n=15
|
||||
)
|
||||
|
||||
# Extract tickers with volume context
|
||||
prompt = """Extract stock tickers from this unusual volume report with context about the accumulation pattern.
|
||||
|
||||
For each ticker, include:
|
||||
- ticker: The stock symbol (1-5 uppercase letters)
|
||||
- context: Volume multiple, price change, and any interpretation of the pattern
|
||||
|
||||
Unusual Volume Report:
|
||||
{report}
|
||||
|
||||
Return a JSON object with a 'candidates' array of objects, each having 'ticker' and 'context' fields.""".format(report=volume_report)
|
||||
|
||||
structured_llm = self.quick_thinking_llm.with_structured_output(
|
||||
schema=TickerContextList.model_json_schema(),
|
||||
method="json_schema"
|
||||
)
|
||||
response = structured_llm.invoke([HumanMessage(content=prompt)])
|
||||
|
||||
volume_candidates = response.get("candidates", [])
|
||||
for c in volume_candidates:
|
||||
ticker = c.get("ticker", "").upper().strip()
|
||||
context = c.get("context", "Unusual volume pattern")
|
||||
if re.match(r'^[A-Z]{1,5}$', ticker):
|
||||
try:
|
||||
if execute_tool("validate_ticker", symbol=ticker):
|
||||
candidates.append({"ticker": ticker, "source": "unusual_volume", "context": context})
|
||||
except: pass
|
||||
|
||||
print(f" Found {len(volume_candidates)} unusual volume candidates")
|
||||
except Exception as e:
|
||||
print(f" Error fetching Unusual Volume: {e}")
|
||||
|
||||
# 7. Analyst Rating Changes (Institutional Catalyst)
|
||||
try:
|
||||
analyst_report = execute_tool(
|
||||
"get_analyst_rating_changes",
|
||||
lookback_days=7,
|
||||
change_types=["upgrade", "initiated"], # Focus on positive catalysts
|
||||
top_n=15
|
||||
)
|
||||
|
||||
# Extract tickers with analyst context
|
||||
prompt = """Extract stock tickers from this analyst rating changes report with context about the rating action.
|
||||
|
||||
For each ticker, include:
|
||||
- ticker: The stock symbol (1-5 uppercase letters)
|
||||
- context: Type of change (upgrade/initiated), analyst firm, price target, and any other relevant details
|
||||
|
||||
Analyst Rating Changes:
|
||||
{report}
|
||||
|
||||
Return a JSON object with a 'candidates' array of objects, each having 'ticker' and 'context' fields.""".format(report=analyst_report)
|
||||
|
||||
structured_llm = self.quick_thinking_llm.with_structured_output(
|
||||
schema=TickerContextList.model_json_schema(),
|
||||
method="json_schema"
|
||||
)
|
||||
response = structured_llm.invoke([HumanMessage(content=prompt)])
|
||||
|
||||
analyst_candidates = response.get("candidates", [])
|
||||
for c in analyst_candidates:
|
||||
ticker = c.get("ticker", "").upper().strip()
|
||||
context = c.get("context", "Recent analyst action")
|
||||
if re.match(r'^[A-Z]{1,5}$', ticker):
|
||||
try:
|
||||
if execute_tool("validate_ticker", symbol=ticker):
|
||||
candidates.append({"ticker": ticker, "source": "analyst_upgrade", "context": context})
|
||||
except: pass
|
||||
|
||||
print(f" Found {len(analyst_candidates)} analyst upgrade candidates")
|
||||
except Exception as e:
|
||||
print(f" Error fetching Analyst Ratings: {e}")
|
||||
|
||||
# Deduplicate
|
||||
unique_candidates = {}
|
||||
for c in candidates:
|
||||
|
|
@ -425,8 +525,8 @@ Return a JSON object with a 'candidates' array of objects, each having 'ticker'
|
|||
from datetime import datetime
|
||||
today = datetime.now().strftime("%Y-%m-%d")
|
||||
|
||||
# Get RSI
|
||||
rsi_data = execute_tool("get_indicators", symbol=ticker, indicator="rsi", curr_date=today, look_back_days=14)
|
||||
# Get RSI (and other indicators)
|
||||
rsi_data = execute_tool("get_indicators", symbol=ticker, curr_date=today)
|
||||
|
||||
# Simple parsing of the string report to find the latest value
|
||||
# The report format is usually "## rsi values...\n\nDATE: VALUE"
|
||||
|
|
|
|||
|
|
@ -16,6 +16,7 @@ from typing import Dict, List, Optional, Callable, Any
|
|||
from tradingagents.dataflows.y_finance import (
|
||||
get_YFin_data_online,
|
||||
get_stock_stats_indicators_window,
|
||||
get_technical_analysis,
|
||||
get_balance_sheet as get_yfinance_balance_sheet,
|
||||
get_cashflow as get_yfinance_cashflow,
|
||||
get_income_statement as get_yfinance_income_statement,
|
||||
|
|
@ -116,24 +117,34 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = {
|
|||
|
||||
# ========== TECHNICAL INDICATORS ==========
|
||||
|
||||
# "get_indicators": {
|
||||
# "description": "Get concise technical analysis with signals, trends, and key indicator interpretations",
|
||||
# "category": "technical_indicators",
|
||||
# "agents": ["market"],
|
||||
# "vendors": {
|
||||
# "yfinance": get_technical_analysis,
|
||||
# },
|
||||
# "vendor_priority": ["yfinance"],
|
||||
# "parameters": {
|
||||
# "symbol": {"type": "str", "description": "Ticker symbol"},
|
||||
# "curr_date": {"type": "str", "description": "Current trading date, YYYY-mm-dd"},
|
||||
# },
|
||||
# "returns": "str: Concise analysis with RSI signals, MACD crossovers, MA trends, Bollinger position, and ATR volatility",
|
||||
# },
|
||||
|
||||
"get_indicators": {
|
||||
"description": "Retrieve technical indicators for a given ticker symbol",
|
||||
"description": "Get concise technical analysis with signals, trends, and key indicator interpretations",
|
||||
"category": "technical_indicators",
|
||||
"agents": ["market"],
|
||||
"vendors": {
|
||||
"yfinance": get_stock_stats_indicators_window,
|
||||
"alpha_vantage": get_alpha_vantage_indicator,
|
||||
"yfinance": get_technical_analysis,
|
||||
},
|
||||
"vendor_priority": ["yfinance"],
|
||||
"execution_mode": "aggregate",
|
||||
"aggregate_vendors": ["yfinance"],
|
||||
"parameters": {
|
||||
"symbol": {"type": "str", "description": "Ticker symbol"},
|
||||
"indicator": {"type": "str", "description": "Technical indicator (rsi, macd, sma, ema, etc.)"},
|
||||
"curr_date": {"type": "str", "description": "Current trading date, YYYY-mm-dd"},
|
||||
"look_back_days": {"type": "int", "description": "Days to look back", "default": 30},
|
||||
},
|
||||
"returns": "str: Formatted report containing technical indicators",
|
||||
"returns": "str: Concise analysis with RSI signals, MACD crossovers, MA trends, Bollinger position, and ATR volatility",
|
||||
},
|
||||
|
||||
# ========== FUNDAMENTAL DATA ==========
|
||||
|
|
@ -226,9 +237,9 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = {
|
|||
"openai": get_stock_news_openai,
|
||||
"google": get_google_news,
|
||||
},
|
||||
"vendor_priority": ["alpha_vantage", "reddit", "openai", "google"],
|
||||
"vendor_priority": ["reddit", "openai", "google"],
|
||||
"execution_mode": "aggregate",
|
||||
"aggregate_vendors": ["alpha_vantage", "reddit", "google"],
|
||||
"aggregate_vendors": ["reddit", "openai", "google"],
|
||||
"parameters": {
|
||||
"query": {"type": "str", "description": "Search query or ticker symbol"},
|
||||
"start_date": {"type": "str", "description": "Start date, yyyy-mm-dd"},
|
||||
|
|
@ -262,9 +273,10 @@ TOOL_REGISTRY: Dict[str, Dict[str, Any]] = {
|
|||
"category": "news_data",
|
||||
"agents": ["news"],
|
||||
"vendors": {
|
||||
"yfinance": get_yfinance_insider_transactions,
|
||||
"alpha_vantage": get_alpha_vantage_insider_transactions,
|
||||
},
|
||||
"vendor_priority": ["alpha_vantage"],
|
||||
"vendor_priority": ["yfinance"],
|
||||
"parameters": {
|
||||
"ticker": {"type": "str", "description": "Ticker symbol"},
|
||||
},
|
||||
|
|
|
|||
Loading…
Reference in New Issue