diff --git a/README.md b/README.md
index cac18691..0cb24c9a 100644
--- a/README.md
+++ b/README.md
@@ -1,52 +1,5 @@
-
- 
-
-
-
-
-
-
----
-
# TradingAgents: Multi-Agents LLM Financial Trading Framework
-> 🎉 **TradingAgents** officially released! We have received numerous inquiries about the work, and we would like to express our thanks for the enthusiasm in our community.
->
-> So we decided to fully open-source the framework. Looking forward to building impactful projects with you!
-
-
-
-
-
-🚀 [TradingAgents](#tradingagents-framework) | ⚡ [Installation & CLI](#installation-and-cli) | 🎬 [Demo](https://www.youtube.com/watch?v=90gr5lwjIho) | 📦 [Package Usage](#tradingagents-package) | 🤝 [Contributing](#contributing) | 📄 [Citation](#citation)
-
-
-
## TradingAgents Framework
TradingAgents is a multi-agent trading framework that mirrors the dynamics of real-world trading firms. By deploying specialized LLM-powered agents: from fundamental analysts, sentiment experts, and technical analysts, to trader, risk management team, the platform collaboratively evaluates market conditions and informs trading decisions. Moreover, these agents engage in dynamic discussions to pinpoint the optimal strategy.
@@ -154,60 +107,17 @@ We built TradingAgents with LangGraph to ensure flexibility and modularity. We u
### Python Usage
-To use TradingAgents inside your code, you can import the `tradingagents` module and initialize a `TradingAgentsGraph()` object. The `.propagate()` function will return a decision. You can run `main.py`, here's also a quick example:
-
-```python
-from tradingagents.graph.trading_graph import TradingAgentsGraph
-from tradingagents.default_config import DEFAULT_CONFIG
-
-ta = TradingAgentsGraph(debug=True, config=DEFAULT_CONFIG.copy())
-
-# forward propagate
-_, decision = ta.propagate("NVDA", "2024-05-10")
-print(decision)
-```
-
-You can also adjust the default configuration to set your own choice of LLMs, debate rounds, etc.
-
-```python
-from tradingagents.graph.trading_graph import TradingAgentsGraph
-from tradingagents.default_config import DEFAULT_CONFIG
-
-# Create a custom config
-config = DEFAULT_CONFIG.copy()
-config["deep_think_llm"] = "gpt-4.1-nano" # Use a different model
-config["quick_think_llm"] = "gpt-4.1-nano" # Use a different model
-config["max_debate_rounds"] = 1 # Increase debate rounds
-config["online_tools"] = True # Use online tools or cached data
-
-# Initialize with custom config
-ta = TradingAgentsGraph(debug=True, config=config)
-
-# forward propagate
-_, decision = ta.propagate("NVDA", "2024-05-10")
-print(decision)
-```
+Basic execution of the repository can be found in the following [notebook.](./scripts/basic_run.ipynb)
> For `online_tools`, we recommend enabling them for experimentation, as they provide access to real-time data. The agents' offline tools rely on cached data from our **Tauric TradingDB**, a curated dataset we use for backtesting. We're currently in the process of refining this dataset, and we plan to release it soon alongside our upcoming projects. Stay tuned!
-You can view the full list of configurations in `tradingagents/default_config.py`.
+You can view the full list of configurations in [tradingagents/default_config.py](./tradingagents/default_config.py).
-## Contributing
+## Customization possibilities
-We welcome contributions from the community! Whether it's fixing a bug, improving documentation, or suggesting a new feature, your input helps make this project better. If you are interested in this line of research, please consider joining our open-source financial AI research community [Tauric Research](https://tauric.ai/).
+### Current Online tools
-## Citation
-
-Please reference our work if you find *TradingAgents* provides you with some help :)
-
-```
-@misc{xiao2025tradingagentsmultiagentsllmfinancial,
- title={TradingAgents: Multi-Agents LLM Financial Trading Framework},
- author={Yijia Xiao and Edward Sun and Di Luo and Wei Wang},
- year={2025},
- eprint={2412.20138},
- archivePrefix={arXiv},
- primaryClass={q-fin.TR},
- url={https://arxiv.org/abs/2412.20138},
-}
-```
+- Market
+- Social
+- News
+- Fundamentals
\ No newline at end of file
diff --git a/scripts/basic_run.ipynb b/scripts/basic_run.ipynb
index 0cb69e18..9519de52 100644
--- a/scripts/basic_run.ipynb
+++ b/scripts/basic_run.ipynb
@@ -1401,6 +1401,14 @@
"print(decision)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e7770fe3",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "code",
"execution_count": null,
diff --git a/scripts/visualise_output.ipynb b/scripts/visualise_output.ipynb
new file mode 100644
index 00000000..bc10aa2f
--- /dev/null
+++ b/scripts/visualise_output.ipynb
@@ -0,0 +1,1882 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "0aab70ec",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "import plotly.graph_objects as go\n",
+ "import plotly.express as px\n",
+ "from plotly.subplots import make_subplots\n",
+ "import pandas as pd\n",
+ "import re\n",
+ "from datetime import datetime\n",
+ "from textstat import flesch_reading_ease\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "47f14ad4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class InvestmentDashboard:\n",
+ " def __init__(self, json_data=None, json_file_path=None):\n",
+ " \"\"\"\n",
+ " Initialize the dashboard with JSON data\n",
+ " Args:\n",
+ " json_data: Dictionary containing investment data\n",
+ " json_file_path: Path to JSON file containing investment data\n",
+ " \"\"\"\n",
+ " if json_file_path:\n",
+ " with open(json_file_path, 'r') as f:\n",
+ " self.data = json.load(f)\n",
+ " elif json_data:\n",
+ " self.data = json_data\n",
+ " else:\n",
+ " raise ValueError(\"Either json_data or json_file_path must be provided\")\n",
+ " \n",
+ " self.analysis = self._analyze_data()\n",
+ " \n",
+ " def _analyze_data(self):\n",
+ " \"\"\"Analyze the JSON data to extract key metrics and insights\"\"\"\n",
+ " analysis = {}\n",
+ " \n",
+ " for date, data in self.data.items():\n",
+ " company = data.get('company_of_interest', 'Unknown')\n",
+ " \n",
+ " # Extract technical indicators from market report\n",
+ " analysis[date] = {\n",
+ " 'company': company,\n",
+ " 'trade_date': data.get('trade_date'),\n",
+ " 'technical_indicators': self._extract_technical_indicators(data.get('market_report', '')),\n",
+ " 'sentiment_metrics': self._extract_sentiment_metrics(data.get('sentiment_report', '')),\n",
+ " 'fundamental_metrics': self._extract_fundamental_metrics(data.get('fundamentals_report', '')),\n",
+ " 'final_decisions': self._extract_decisions(data),\n",
+ " 'risk_assessment': self._analyze_risk_debate(data.get('risk_debate_state', {})),\n",
+ " 'news_sentiment': self._analyze_news_sentiment(data.get('news_report', ''))\n",
+ " }\n",
+ " \n",
+ " return analysis\n",
+ " \n",
+ " def _extract_technical_indicators(self, market_report):\n",
+ " \"\"\"Extract technical indicators from market report\"\"\"\n",
+ " indicators = {}\n",
+ " \n",
+ " # Price information\n",
+ " price_match = re.search(r'closing price.*?\\$?([\\d,]+\\.?\\d*)', market_report, re.IGNORECASE)\n",
+ " if price_match:\n",
+ " indicators['closing_price'] = float(price_match.group(1).replace(',', ''))\n",
+ " \n",
+ " # Moving averages\n",
+ " sma_50_match = re.search(r'50-SMA.*?\\$?([\\d,]+\\.?\\d*)', market_report, re.IGNORECASE)\n",
+ " if sma_50_match:\n",
+ " indicators['sma_50'] = float(sma_50_match.group(1).replace(',', ''))\n",
+ " \n",
+ " sma_200_match = re.search(r'200-SMA.*?\\$?([\\d,]+\\.?\\d*)', market_report, re.IGNORECASE)\n",
+ " if sma_200_match:\n",
+ " indicators['sma_200'] = float(sma_200_match.group(1).replace(',', ''))\n",
+ " \n",
+ " ema_10_match = re.search(r'10-EMA.*?\\$?([\\d,]+\\.?\\d*)', market_report, re.IGNORECASE)\n",
+ " if ema_10_match:\n",
+ " indicators['ema_10'] = float(ema_10_match.group(1).replace(',', ''))\n",
+ " \n",
+ " # MACD\n",
+ " macd_match = re.search(r'MACD.*?([\\d.]+)', market_report, re.IGNORECASE)\n",
+ " if macd_match:\n",
+ " indicators['macd'] = float(macd_match.group(1))\n",
+ " \n",
+ " # RSI\n",
+ " rsi_match = re.search(r'RSI.*?([\\d.]+)', market_report, re.IGNORECASE)\n",
+ " if rsi_match:\n",
+ " indicators['rsi'] = float(rsi_match.group(1))\n",
+ " \n",
+ " # ATR\n",
+ " atr_match = re.search(r'ATR.*?([\\d.]+)', market_report, re.IGNORECASE)\n",
+ " if atr_match:\n",
+ " indicators['atr'] = float(atr_match.group(1))\n",
+ " \n",
+ " return indicators\n",
+ " \n",
+ " def _extract_sentiment_metrics(self, sentiment_report):\n",
+ " \"\"\"Extract sentiment metrics from sentiment report\"\"\"\n",
+ " metrics = {}\n",
+ " \n",
+ " # Reddit mentions\n",
+ " mentions_match = re.search(r'(\\d+)\\s+mentions', sentiment_report, re.IGNORECASE)\n",
+ " if mentions_match:\n",
+ " metrics['reddit_mentions'] = int(mentions_match.group(1))\n",
+ " \n",
+ " # Sentiment score\n",
+ " sentiment_match = re.search(r'sentiment score.*?(\\d+\\.?\\d*)%?', sentiment_report, re.IGNORECASE)\n",
+ " if sentiment_match:\n",
+ " metrics['sentiment_score'] = float(sentiment_match.group(1))\n",
+ " \n",
+ " # Rule of 40 score\n",
+ " rule40_match = re.search(r'Rule of 40.*?(\\d+)%', sentiment_report, re.IGNORECASE)\n",
+ " if rule40_match:\n",
+ " metrics['rule_of_40'] = int(rule40_match.group(1))\n",
+ " \n",
+ " # Stock price from sentiment\n",
+ " price_match = re.search(r'current price.*?\\$?([\\d,]+\\.?\\d*)', sentiment_report, re.IGNORECASE)\n",
+ " if price_match:\n",
+ " metrics['current_price'] = float(price_match.group(1).replace(',', ''))\n",
+ " \n",
+ " return metrics\n",
+ " \n",
+ " def _extract_fundamental_metrics(self, fundamentals_report):\n",
+ " \"\"\"Extract fundamental metrics from fundamentals report\"\"\"\n",
+ " metrics = {}\n",
+ " \n",
+ " # Revenue\n",
+ " revenue_match = re.search(r'revenue.*?\\$?([\\d,]+\\.?\\d*)\\s*(billion|million)', fundamentals_report, re.IGNORECASE)\n",
+ " if revenue_match:\n",
+ " value = float(revenue_match.group(1).replace(',', ''))\n",
+ " unit = revenue_match.group(2).lower()\n",
+ " if unit == 'billion':\n",
+ " value *= 1000\n",
+ " metrics['revenue_millions'] = value\n",
+ " \n",
+ " # Revenue growth\n",
+ " revenue_growth_match = re.search(r'(\\d+)%.*?year-over-year', fundamentals_report, re.IGNORECASE)\n",
+ " if revenue_growth_match:\n",
+ " metrics['revenue_growth'] = int(revenue_growth_match.group(1))\n",
+ " \n",
+ " # Net income\n",
+ " net_income_match = re.search(r'net income.*?\\$?([\\d,]+\\.?\\d*)\\s*million', fundamentals_report, re.IGNORECASE)\n",
+ " if net_income_match:\n",
+ " metrics['net_income_millions'] = float(net_income_match.group(1).replace(',', ''))\n",
+ " \n",
+ " # Market cap\n",
+ " market_cap_match = re.search(r'market capitalization.*?\\$?([\\d,]+\\.?\\d*)\\s*(billion|trillion)', fundamentals_report, re.IGNORECASE)\n",
+ " if market_cap_match:\n",
+ " value = float(market_cap_match.group(1).replace(',', ''))\n",
+ " unit = market_cap_match.group(2).lower()\n",
+ " if unit == 'trillion':\n",
+ " value *= 1000\n",
+ " metrics['market_cap_billions'] = value\n",
+ " \n",
+ " return metrics\n",
+ " \n",
+ " def _extract_decisions(self, data):\n",
+ " \"\"\"Extract investment decisions from various reports\"\"\"\n",
+ " decisions = {}\n",
+ " \n",
+ " # Extract final decisions from each report\n",
+ " for report_type in ['market_report', 'sentiment_report', 'fundamentals_report']:\n",
+ " report = data.get(report_type, '')\n",
+ " decision_match = re.search(r'FINAL TRANSACTION PROPOSAL:\\s*\\*?\\*?([A-Z]+)', report, re.IGNORECASE)\n",
+ " if decision_match:\n",
+ " decisions[report_type] = decision_match.group(1).upper()\n",
+ " \n",
+ " # Final trade decision\n",
+ " final_decision = data.get('final_trade_decision', '')\n",
+ " if 'BUY' in final_decision.upper():\n",
+ " decisions['final_decision'] = 'BUY'\n",
+ " elif 'SELL' in final_decision.upper():\n",
+ " decisions['final_decision'] = 'SELL'\n",
+ " elif 'HOLD' in final_decision.upper():\n",
+ " decisions['final_decision'] = 'HOLD'\n",
+ " \n",
+ " return decisions\n",
+ " \n",
+ " def _analyze_risk_debate(self, risk_debate):\n",
+ " \"\"\"Analyze risk debate to extract risk assessment\"\"\"\n",
+ " if not risk_debate:\n",
+ " return {}\n",
+ " \n",
+ " risk_analysis = {}\n",
+ " \n",
+ " # Analyze judge decision\n",
+ " judge_decision = risk_debate.get('judge_decision', '')\n",
+ " if 'BUY' in judge_decision.upper():\n",
+ " risk_analysis['risk_adjusted_decision'] = 'BUY'\n",
+ " elif 'SELL' in judge_decision.upper():\n",
+ " risk_analysis['risk_adjusted_decision'] = 'SELL'\n",
+ " elif 'HOLD' in judge_decision.upper():\n",
+ " risk_analysis['risk_adjusted_decision'] = 'HOLD'\n",
+ " \n",
+ " # Extract position sizing\n",
+ " position_match = re.search(r'(\\d+\\.?\\d*)%.*?portfolio', judge_decision, re.IGNORECASE)\n",
+ " if position_match:\n",
+ " risk_analysis['recommended_position_size'] = float(position_match.group(1))\n",
+ " \n",
+ " # Extract stop loss\n",
+ " stop_loss_match = re.search(r'(\\d+)%.*?stop', judge_decision, re.IGNORECASE)\n",
+ " if stop_loss_match:\n",
+ " risk_analysis['stop_loss_percent'] = int(stop_loss_match.group(1))\n",
+ " \n",
+ " return risk_analysis\n",
+ " \n",
+ " def _analyze_news_sentiment(self, news_report):\n",
+ " \"\"\"Analyze news report for sentiment indicators\"\"\"\n",
+ " sentiment = {}\n",
+ " \n",
+ " # Count positive/negative words\n",
+ " positive_words = ['growth', 'increase', 'strong', 'positive', 'bullish', 'surge', 'rise', 'improvement']\n",
+ " negative_words = ['decline', 'fall', 'weak', 'negative', 'bearish', 'drop', 'concern', 'risk']\n",
+ " \n",
+ " news_lower = news_report.lower()\n",
+ " positive_count = sum(news_lower.count(word) for word in positive_words)\n",
+ " negative_count = sum(news_lower.count(word) for word in negative_words)\n",
+ " \n",
+ " if positive_count + negative_count > 0:\n",
+ " sentiment['news_sentiment_score'] = (positive_count - negative_count) / (positive_count + negative_count) * 100\n",
+ " else:\n",
+ " sentiment['news_sentiment_score'] = 0\n",
+ " \n",
+ " return sentiment\n",
+ " \n",
+ " def create_dashboard(self):\n",
+ " \"\"\"Create the main dashboard with multiple visualizations\"\"\"\n",
+ " fig = make_subplots(\n",
+ " rows=4, cols=2,\n",
+ " subplot_titles=(\n",
+ " 'Technical Indicators Overview', 'Investment Decisions Summary',\n",
+ " 'Sentiment Analysis', 'Fundamental Metrics',\n",
+ " 'Risk Assessment', 'Price vs Moving Averages',\n",
+ " 'Decision Consensus', 'Key Performance Metrics'\n",
+ " ),\n",
+ " specs=[[{\"secondary_y\": True}, {\"type\": \"pie\"}],\n",
+ " [{\"type\": \"bar\"}, {\"type\": \"bar\"}],\n",
+ " [{\"type\": \"indicator\"}, {\"type\": \"scatter\"}],\n",
+ " [{\"type\": \"bar\"}, {\"type\": \"table\"}]],\n",
+ " vertical_spacing=0.12,\n",
+ " horizontal_spacing=0.1\n",
+ " )\n",
+ " \n",
+ " # Extract data for visualizations\n",
+ " dates = list(self.analysis.keys())\n",
+ " latest_date = dates[0] if dates else None\n",
+ " \n",
+ " if not latest_date:\n",
+ " return fig\n",
+ " \n",
+ " latest_data = self.analysis[latest_date]\n",
+ " \n",
+ " # 1. Technical Indicators Radar Chart (converted to bar for simplicity)\n",
+ " tech_indicators = latest_data['technical_indicators']\n",
+ " if tech_indicators:\n",
+ " # RSI and normalized values for comparison\n",
+ " indicators = []\n",
+ " values = []\n",
+ " \n",
+ " if 'rsi' in tech_indicators:\n",
+ " indicators.append('RSI')\n",
+ " values.append(tech_indicators['rsi'])\n",
+ " \n",
+ " if 'macd' in tech_indicators:\n",
+ " indicators.append('MACD')\n",
+ " values.append(tech_indicators['macd'] * 10) # Scale for visibility\n",
+ " \n",
+ " if 'atr' in tech_indicators:\n",
+ " indicators.append('ATR')\n",
+ " values.append(tech_indicators['atr'] * 10) # Scale for visibility\n",
+ " \n",
+ " if indicators:\n",
+ " fig.add_trace(\n",
+ " go.Bar(x=indicators, y=values, name=\"Technical Indicators\", \n",
+ " marker_color=['#FF6B6B', '#4ECDC4', '#45B7D1']),\n",
+ " row=1, col=1\n",
+ " )\n",
+ " \n",
+ " # 2. Investment Decisions Pie Chart\n",
+ " decisions = latest_data['final_decisions']\n",
+ " decision_counts = {}\n",
+ " for decision in decisions.values():\n",
+ " decision_counts[decision] = decision_counts.get(decision, 0) + 1\n",
+ " \n",
+ " if decision_counts:\n",
+ " fig.add_trace(\n",
+ " go.Pie(labels=list(decision_counts.keys()), \n",
+ " values=list(decision_counts.values()),\n",
+ " hole=0.4,\n",
+ " marker_colors=['#FF6B6B', '#4ECDC4', '#45B7D1']),\n",
+ " row=1, col=2\n",
+ " )\n",
+ " \n",
+ " # 3. Sentiment Analysis\n",
+ " sentiment_data = latest_data['sentiment_metrics']\n",
+ " if sentiment_data:\n",
+ " sentiment_labels = []\n",
+ " sentiment_values = []\n",
+ " \n",
+ " if 'reddit_mentions' in sentiment_data:\n",
+ " sentiment_labels.append('Reddit Mentions')\n",
+ " sentiment_values.append(sentiment_data['reddit_mentions'])\n",
+ " \n",
+ " if 'sentiment_score' in sentiment_data:\n",
+ " sentiment_labels.append('Sentiment Score')\n",
+ " sentiment_values.append(sentiment_data['sentiment_score'])\n",
+ " \n",
+ " if sentiment_labels:\n",
+ " fig.add_trace(\n",
+ " go.Bar(x=sentiment_labels, y=sentiment_values, \n",
+ " name=\"Sentiment Metrics\",\n",
+ " marker_color='#FFA726'),\n",
+ " row=2, col=1\n",
+ " )\n",
+ " \n",
+ " # 4. Fundamental Metrics\n",
+ " fundamental_data = latest_data['fundamental_metrics']\n",
+ " if fundamental_data:\n",
+ " fund_labels = []\n",
+ " fund_values = []\n",
+ " \n",
+ " if 'revenue_growth' in fundamental_data:\n",
+ " fund_labels.append('Revenue Growth %')\n",
+ " fund_values.append(fundamental_data['revenue_growth'])\n",
+ " \n",
+ " if 'revenue_millions' in fundamental_data:\n",
+ " fund_labels.append('Revenue (M)')\n",
+ " fund_values.append(fundamental_data['revenue_millions'])\n",
+ " \n",
+ " if fund_labels:\n",
+ " fig.add_trace(\n",
+ " go.Bar(x=fund_labels, y=fund_values, \n",
+ " name=\"Fundamental Metrics\",\n",
+ " marker_color='#66BB6A'),\n",
+ " row=2, col=2\n",
+ " )\n",
+ " \n",
+ " # 5. Risk Assessment Gauge\n",
+ " risk_data = latest_data['risk_assessment']\n",
+ " risk_score = 50 # Default neutral\n",
+ " \n",
+ " if 'recommended_position_size' in risk_data:\n",
+ " # Convert position size to risk score (higher position = higher confidence = lower risk)\n",
+ " position_size = risk_data['recommended_position_size']\n",
+ " risk_score = max(0, min(100, 100 - (position_size * 10)))\n",
+ " \n",
+ " fig.add_trace(\n",
+ " go.Indicator(\n",
+ " mode=\"gauge+number+delta\",\n",
+ " value=risk_score,\n",
+ " title={'text': \"Risk Level\"},\n",
+ " gauge={\n",
+ " 'axis': {'range': [0, 100]},\n",
+ " 'bar': {'color': \"#FF6B6B\"},\n",
+ " 'steps': [\n",
+ " {'range': [0, 30], 'color': \"#4ECDC4\"},\n",
+ " {'range': [30, 70], 'color': \"#FFA726\"},\n",
+ " {'range': [70, 100], 'color': \"#FF6B6B\"}\n",
+ " ],\n",
+ " 'threshold': {\n",
+ " 'line': {'color': \"red\", 'width': 4},\n",
+ " 'thickness': 0.75,\n",
+ " 'value': 80\n",
+ " }\n",
+ " }\n",
+ " ),\n",
+ " row=3, col=1\n",
+ " )\n",
+ " \n",
+ " # 6. Price vs Moving Averages\n",
+ " if 'closing_price' in tech_indicators:\n",
+ " price_data = {\n",
+ " 'Current Price': tech_indicators.get('closing_price', 0),\n",
+ " '50-SMA': tech_indicators.get('sma_50', 0),\n",
+ " '200-SMA': tech_indicators.get('sma_200', 0),\n",
+ " '10-EMA': tech_indicators.get('ema_10', 0)\n",
+ " }\n",
+ " \n",
+ " # Filter out zero values\n",
+ " price_data = {k: v for k, v in price_data.items() if v > 0}\n",
+ " \n",
+ " if price_data:\n",
+ " fig.add_trace(\n",
+ " go.Scatter(x=list(price_data.keys()), \n",
+ " y=list(price_data.values()),\n",
+ " mode='lines+markers',\n",
+ " name=\"Price vs MA\",\n",
+ " line=dict(color='#9C27B0', width=3),\n",
+ " marker=dict(size=8)),\n",
+ " row=3, col=2\n",
+ " )\n",
+ " \n",
+ " # 7. Decision Consensus\n",
+ " all_decisions = list(decisions.values())\n",
+ " buy_count = all_decisions.count('BUY')\n",
+ " hold_count = all_decisions.count('HOLD')\n",
+ " sell_count = all_decisions.count('SELL')\n",
+ " \n",
+ " fig.add_trace(\n",
+ " go.Bar(x=['BUY', 'HOLD', 'SELL'], \n",
+ " y=[buy_count, hold_count, sell_count],\n",
+ " name=\"Decision Consensus\",\n",
+ " marker_color=['#4ECDC4', '#FFA726', '#FF6B6B']),\n",
+ " row=4, col=1\n",
+ " )\n",
+ " \n",
+ " # 8. Key Performance Metrics Table\n",
+ " kpi_data = []\n",
+ " \n",
+ " # Compile KPI data\n",
+ " if 'closing_price' in tech_indicators:\n",
+ " kpi_data.append(['Current Price', f\"${tech_indicators['closing_price']:.2f}\"])\n",
+ " if 'revenue_growth' in fundamental_data:\n",
+ " kpi_data.append(['Revenue Growth', f\"{fundamental_data['revenue_growth']}%\"])\n",
+ " if 'rule_of_40' in sentiment_data:\n",
+ " kpi_data.append(['Rule of 40', f\"{sentiment_data['rule_of_40']}%\"])\n",
+ " if 'rsi' in tech_indicators:\n",
+ " kpi_data.append(['RSI', f\"{tech_indicators['rsi']:.2f}\"])\n",
+ " \n",
+ " if kpi_data:\n",
+ " fig.add_trace(\n",
+ " go.Table(\n",
+ " header=dict(values=['Metric', 'Value'],\n",
+ " fill_color='#E3F2FD',\n",
+ " align='left',\n",
+ " font=dict(size=12, color='#1976D2')),\n",
+ " cells=dict(values=[[row[0] for row in kpi_data], \n",
+ " [row[1] for row in kpi_data]],\n",
+ " fill_color='#F8F9FA',\n",
+ " align='left',\n",
+ " font=dict(size=11))\n",
+ " ),\n",
+ " row=4, col=2\n",
+ " )\n",
+ " \n",
+ " # Update layout\n",
+ " fig.update_layout(\n",
+ " title=f\"Investment Analysis Dashboard - {latest_data['company']} ({latest_date})\",\n",
+ " title_font_size=24,\n",
+ " title_x=0.5,\n",
+ " height=1200,\n",
+ " showlegend=False,\n",
+ " plot_bgcolor='white',\n",
+ " paper_bgcolor='#F8F9FA',\n",
+ " font=dict(family=\"Arial, sans-serif\", size=10)\n",
+ " )\n",
+ " \n",
+ " # Update all axes\n",
+ " fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='#E0E0E0')\n",
+ " fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='#E0E0E0')\n",
+ " \n",
+ " return fig\n",
+ " \n",
+ " def create_summary_report(self):\n",
+ " \"\"\"Create a summary report of the analysis\"\"\"\n",
+ " report = {}\n",
+ " \n",
+ " for date, analysis in self.analysis.items():\n",
+ " company = analysis['company']\n",
+ " decisions = analysis['final_decisions']\n",
+ " \n",
+ " # Get most common decision\n",
+ " decision_list = list(decisions.values())\n",
+ " final_recommendation = max(set(decision_list), key=decision_list.count) if decision_list else \"UNKNOWN\"\n",
+ " \n",
+ " # Risk assessment\n",
+ " risk_data = analysis['risk_assessment']\n",
+ " position_size = risk_data.get('recommended_position_size', 'Not specified')\n",
+ " stop_loss = risk_data.get('stop_loss_percent', 'Not specified')\n",
+ " \n",
+ " # Key metrics\n",
+ " tech_indicators = analysis['technical_indicators']\n",
+ " fundamental_metrics = analysis['fundamental_metrics']\n",
+ " \n",
+ " report[date] = {\n",
+ " 'company': company,\n",
+ " 'final_recommendation': final_recommendation,\n",
+ " 'current_price': tech_indicators.get('closing_price', 'N/A'),\n",
+ " 'rsi': tech_indicators.get('rsi', 'N/A'),\n",
+ " 'revenue_growth': fundamental_metrics.get('revenue_growth', 'N/A'),\n",
+ " 'position_size': position_size,\n",
+ " 'stop_loss': stop_loss\n",
+ " }\n",
+ " \n",
+ " return report"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "ff0c9dd5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "# Example usage and demo\n",
+ "def demo_dashboard():\n",
+ " \"\"\"Demo function to show how to use the dashboard\"\"\"\n",
+ " \n",
+ " # Sample JSON data structure (you can replace this with actual data)\n",
+ " sample_data = {\n",
+ " \"2025-08-01\": {\n",
+ " \"company_of_interest\": \"PLTR\",\n",
+ " \"trade_date\": \"2025-08-01\",\n",
+ " \"market_report\": \"The closing price on August 1, 2025, is $158.35. 50-SMA: $139.85, 200-SMA: $96.60, 10-EMA: $155.04, MACD: 5.46, RSI: 58.63, ATR: 5.41 FINAL TRANSACTION PROPOSAL: BUY\",\n",
+ " \"sentiment_report\": \"PLTR received 70 mentions on Reddit with a sentiment score of 58%. Rule of 40 score of 81%. Current price $160.66. FINAL TRANSACTION PROPOSAL: BUY\",\n",
+ " \"fundamentals_report\": \"Q2 2025 Revenue: $1 billion, 48% year-over-year growth. Net Income: $327 million, 144% increase. Market Capitalization: $360 billion. FINAL TRANSACTION PROPOSAL: HOLD\",\n",
+ " \"final_trade_decision\": \"BUY with 2.5% starter position, 12% stop-loss\",\n",
+ " \"risk_debate_state\": {\n",
+ " \"judge_decision\": \"BUY with 2.5% of portfolio at ~$160. Stop-loss at 12% below entry.\"\n",
+ " },\n",
+ " \"news_report\": \"Global M&A activity shows strong growth. Palantir wins $10 billion U.S. Army contract showing positive momentum.\"\n",
+ " }\n",
+ " }\n",
+ " \n",
+ " # Create dashboard\n",
+ " dashboard = InvestmentDashboard(json_data=sample_data)\n",
+ " \n",
+ " # Generate the dashboard\n",
+ " fig = dashboard.create_dashboard()\n",
+ " fig.show()\n",
+ " \n",
+ " # Get summary report\n",
+ " summary = dashboard.create_summary_report()\n",
+ " print(\"Summary Report:\")\n",
+ " print(json.dumps(summary, indent=2))\n",
+ " \n",
+ " return dashboard"
+ ]
+ },
+ {
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+ "id": "f540c44b",
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+ "gridwidth": 1,
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+ "anchor": "y5",
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+ "gridwidth": 1,
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+ "gridcolor": "#E0E0E0",
+ "gridwidth": 1,
+ "overlaying": "y",
+ "showgrid": true,
+ "side": "right"
+ },
+ "yaxis3": {
+ "anchor": "x2",
+ "domain": [
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+ "gridcolor": "#E0E0E0",
+ "gridwidth": 1,
+ "showgrid": true
+ },
+ "yaxis4": {
+ "anchor": "x3",
+ "domain": [
+ 0.56,
+ 0.7200000000000001
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+ "gridcolor": "#E0E0E0",
+ "gridwidth": 1,
+ "showgrid": true
+ },
+ "yaxis5": {
+ "anchor": "x4",
+ "domain": [
+ 0.28,
+ 0.44000000000000006
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+ "gridcolor": "#E0E0E0",
+ "gridwidth": 1,
+ "showgrid": true
+ },
+ "yaxis6": {
+ "anchor": "x5",
+ "domain": [
+ 0,
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+ "gridcolor": "#E0E0E0",
+ "gridwidth": 1,
+ "showgrid": true
+ }
+ }
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Summary Report:\n",
+ "{\n",
+ " \"2025-08-01\": {\n",
+ " \"company\": \"PLTR\",\n",
+ " \"final_recommendation\": \"BUY\",\n",
+ " \"current_price\": 1.0,\n",
+ " \"rsi\": 58.63,\n",
+ " \"revenue_growth\": 48,\n",
+ " \"position_size\": 2.5,\n",
+ " \"stop_loss\": 5\n",
+ " }\n",
+ "}\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<__main__.InvestmentDashboard at 0x7f5fd4fac920>"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Run demo\n",
+ "demo_dashboard()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "35f59ac0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dashboard = InvestmentDashboard(json_file_path=\"path/to/your/file.json\")\n",
+ "fig = dashboard.create_dashboard()\n",
+ "fig.show()\n",
+ "summary = dashboard.create_summary_report()\n",
+ "\n",
+ "\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Trading Agents",
+ "language": "python",
+ "name": "trading_agents"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tradingagents/agents/utils/memory.py b/tradingagents/agents/utils/memory.py
index c5243377..0530793c 100644
--- a/tradingagents/agents/utils/memory.py
+++ b/tradingagents/agents/utils/memory.py
@@ -17,7 +17,6 @@ class FinancialSituationMemory:
except:
# Collection doesn't exist, create it
self.situation_collection = self.chroma_client.create_collection(name=name)
- # self.situation_collection = self.chroma_client.create_collection(name=name)
def get_embedding(self, text):
"""Get OpenAI embedding for a text"""
diff --git a/tradingagents/default_config.py b/tradingagents/default_config.py
index 089e9c24..7b73a55d 100644
--- a/tradingagents/default_config.py
+++ b/tradingagents/default_config.py
@@ -3,7 +3,7 @@ import os
DEFAULT_CONFIG = {
"project_dir": os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
"results_dir": os.getenv("TRADINGAGENTS_RESULTS_DIR", "./results"),
- "data_dir": "/Users/yluo/Documents/Code/ScAI/FR1-data",
+ "data_dir": "/home/brabus61/Desktop/Github Repos/TradingAgents/tradingagents/output_data",
"data_cache_dir": os.path.join(
os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
"dataflows/data_cache",
@@ -14,7 +14,7 @@ DEFAULT_CONFIG = {
"quick_think_llm": "gpt-4o-mini",
"backend_url": "https://api.openai.com/v1",
# Debate and discussion settings
- "max_debate_rounds": 1,
+ "max_debate_rounds": 5,
"max_risk_discuss_rounds": 1,
"max_recur_limit": 100,
# Tool settings
- 
- 
- 
- 
-