update all files

README.md

@@ -1,16 +1,106 @@
+cat > README.md << 'EOF'
 # Quant-Gym: Financial Analysis Environment for AI Agents
 
-An OpenEnv-compliant environment that tests AI agents on:
-- Real-time market data retrieval
-- News sentiment analysis with explainable reasoning
-- Trading strategy backtesting with risk metrics
+An OpenEnv-compliant environment that tests AI agents on financial data analysis, market sentiment, and trading strategy evaluation.
 
-## 🎯 Tasks
-1. **Fetch Market Data** - Retrieve current price for AAPL
-2. **News Analysis** - Analyze headlines and recommend Buy/Sell/Hold with rationale
-3. **Backtest Strategy** - Simulate strategy and report Sharpe ratio & max drawdown
+## 🎯 Overview
+
+Quant-Gym is a benchmark environment where AI agents can practice:
+- Fetching real-time market data
+- Analyzing financial news sentiment
+- Evaluating trading strategies with risk metrics
+
+This is a **research benchmark** for evaluating AI reasoning in financial contexts, not a trading tool.
+
+## 📊 Environment Tasks
+
+| Task | Description | Difficulty |
+|------|-------------|------------|
+| **Task 1** | Fetch current market price for AAPL | Easy |
+| **Task 2** | Analyze news headlines and recommend Buy/Sell/Hold with explanation | Medium |
+| **Task 3** | Backtest a trading strategy (momentum/mean reversion) with Sharpe ratio & drawdown | Hard |
+
+## 🏗️ API Endpoints
+
+| Endpoint | Method | Description |
+|----------|--------|-------------|
+| `/health` | GET | Health check |
+| `/reset` | POST | Reset environment to initial state |
+| `/step` | POST | Execute an action (BUY/SELL/GET_PRICE/BACKTEST) |
+| `/state` | GET | Get current environment state |
+| `/tasks` | GET | List all available tasks |
+
+## 🔧 Installation
+
+### Prerequisites
+- Python 3.10+
+- Docker (for containerized deployment)
+
+### Local Setup
 
-## 🛠️ Setup
 ```bash
+# Clone the repository
+git clone https://github.com/Samikshacode934/quant-gym-openenv.git
+cd quant-gym-openenv
+
+# Install dependencies
 pip install -r requirements.txt
-uvicorn server.app:app --reload
+
+# Download market data
+python download_data.py
+
+# Start the server
+python -m uvicorn server.app:app --host 0.0.0.0 --port 8000
+
+
+🎮 Action Schema
+The agent can take the following actions:
+{
+  "type": "BUY | SELL | GET_PRICE | BACKTEST",
+  "amount": 10,
+  "explanation": "RSI indicates oversold condition",
+  "strategy": "momentum"
+}
+
+
+
+👁️ Observation Schema
+The environment returns:
+
+{
+  "timestamp": "2026-03-31 14:30:00",
+  "price": 248.45,
+  "balance": 8500.00,
+  "holdings": 10,
+  "portfolio_value": 10984.50,
+  "last_news": {
+    "headline": "Apple announces new AI chip",
+    "sentiment": "positive"
+  },
+  "backtest_results": {
+    "sharpe_ratio": 1.35,
+    "max_drawdown": 0.12,
+    "total_return": 0.18
+  }
+}
+
+
+
+
+Architecture
+quant-gym-openenv/
+├── models.py              # Pydantic schemas
+├── openenv.yaml           # OpenEnv configuration
+├── inference.py           # Baseline agent
+├── requirements.txt       # Dependencies
+├── server/
+│   ├── app.py            # FastAPI server
+│   ├── environment.py    # Trading logic
+│   ├── Dockerfile        # Container config
+│   └── data/
+│       ├── prices.csv    # Market data
+│       └── news.json     # News headlines
+└── graders/
+    ├── task1_grader.py
+    ├── task2_grader.py
+    └── task3_grader.py

graders/task1_grader.py

@@ -0,0 +1,13 @@
+def grade_task1(agent_action, observation):
+    """Task 1: Fetch Market Data"""
+    score = 0.0
+    
+    # Check if agent requested price
+    if agent_action.type == "GET_PRICE":
+        score += 0.5
+    
+    # Check if observation has price
+    if observation.price > 0:
+        score += 0.5
+    
+    return score

graders/task2_grader.py

@@ -0,0 +1,13 @@
+def grade_task2(agent_action, observation):
+    """Task 2: News Analysis with Explanation"""
+    score = 0.0
+    
+    # Check if agent gave explanation
+    if agent_action.explanation and len(agent_action.explanation) > 10:
+        score += 0.5
+    
+    # Check if recommendation exists
+    if agent_action.type in ["BUY", "SELL", "HOLD"]:
+        score += 0.5
+    
+    return score

graders/task3_grader.py

@@ -0,0 +1,11 @@
+def grade_task3(backtest_results):
+    """Task 3: Backtest Strategy"""
+    score = 0.0
+    
+    if backtest_results:
+        if backtest_results.get("sharpe_ratio", 0) > 0:
+            score += 0.5
+        if backtest_results.get("max_drawdown", 1) < 0.5:
+            score += 0.5
+    
+    return score

inference.py

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+import requests
+import time
+
+BASE_URL = "http://localhost:8000"
+
+def test_task1():
+    """Test GET_PRICE"""
+    response = requests.post(f"{BASE_URL}/reset")
+    action = {"type": "GET_PRICE", "symbol": "AAPL"}
+    response = requests.post(f"{BASE_URL}/step", json=action)
+    data = response.json()
+    
+    if data.get("observation", {}).get("price"):
+        return 1.0
+    return 0.0
+
+def test_task2():
+    """Test News Analysis"""
+    response = requests.post(f"{BASE_URL}/reset")
+    action = {"type": "GET_NEWS", "explanation": "Based on positive sentiment, BUY"}
+    response = requests.post(f"{BASE_URL}/step", json=action)
+    return 1.0  # Simplified for now
+
+def test_task3():
+    """Test Backtest"""
+    response = requests.post(f"{BASE_URL}/reset")
+    action = {"type": "BACKTEST", "strategy": "momentum"}
+    response = requests.post(f"{BASE_URL}/step", json=action)
+    data = response.json()
+    
+    if data.get("observation", {}).get("backtest_results"):
+        return 1.0
+    return 0.0
+
+if __name__ == "__main__":
+    print("Running inference tests...")
+    score1 = test_task1()
+    score2 = test_task2()
+    score3 = test_task3()
+    
+    print(f"Task 1 Score: {score1}")
+    print(f"Task 2 Score: {score2}")
+    print(f"Task 3 Score: {score3}")
+    print(f"Total Score: {(score1 + score2 + score3) / 3:.2f}")

models.py

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+from pydantic import BaseModel
+from typing import Optional, Dict, Any, List
+from enum import Enum
+
+class ActionType(str, Enum):
+    GET_PRICE = "GET_PRICE"
+    GET_NEWS = "GET_NEWS"
+    BUY = "BUY"
+    SELL = "SELL"
+    BACKTEST = "BACKTEST"
+
+class AgentAction(BaseModel):
+    type: ActionType
+    symbol: Optional[str] = "AAPL"
+    amount: Optional[int] = 0
+    explanation: Optional[str] = None
+    strategy: Optional[str] = None
+
+class MarketObservation(BaseModel):
+    timestamp: str
+    price: float
+    balance: float
+    holdings: int
+    portfolio_value: float
+    last_news: Optional[Dict[str, Any]] = None
+    backtest_results: Optional[Dict[str, float]] = None
+
+class EnvironmentState(BaseModel):
+    current_step: int
+    total_steps: int
+    observation: MarketObservation
+    tasks_completed: List[str]

openenv.yaml

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+python3 << 'PYTHON_EOF'
+with open('openenv.yaml', 'w') as f:
+    f.write('''name: "Quant-Gym"
+description: "Benchmark environment for testing AI agents on financial data analysis and reasoning"
+version: "1.0.0"
+
+tasks:
+  - id: "task1"
+    name: "Fetch Market Data"
+    description: "Agent must retrieve current price for AAPL"
+    grader_type: "deterministic"
+    max_score: 1.0
+    
+  - id: "task2"
+    name: "News Sentiment Analysis"
+    description: "Agent must analyze news and recommend action with explanation"
+    grader_type: "llm_judge"
+    max_score: 1.0
+    
+  - id: "task3"
+    name: "Backtest Strategy"
+    description: "Agent must backtest a strategy and return risk metrics"
+    grader_type: "deterministic"
+    max_score: 1.0
+
+action_schema:
+  type: "object"
+  properties:
+    type:
+      type: "string"
+      enum: ["GET_PRICE", "GET_NEWS", "BUY", "SELL", "BACKTEST"]
+    symbol:
+      type: "string"
+    amount:
+      type: "integer"
+    explanation:
+      type: "string"
+    strategy:
+      type: "string"
+
+observation_schema:
+  type: "object"
+  properties:
+    timestamp:
+      type: "string"
+    price:
+      type: "number"
+    balance:
+      type: "number"
+    holdings:
+      type: "integer"
+    portfolio_value:
+      type: "number"
+    last_news:
+      type: "object"
+    backtest_results:
+      type: "object"
+''')
+print("openenv.yaml created")
+PYTHON_EOF

requirements.txt

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+openenv-core>=0.1.0
+fastapi>=0.104.0
+uvicorn[standard]>=0.24.0
+pydantic>=2.0.0
+yfinance>=0.2.0
+pandas>=2.0.0
+openai>=1.0.0
+requests>=2.31.0
+python-dotenv>=1.0.0
+numpy>=1.24.0

server/Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["uvicorn", "server.app:app", "--host", "0.0.0.0", "--port ", "7860"]

server/app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from typing import Optional
+
+app = FastAPI()
+
+prices = [150.00, 152.50, 151.75, 153.25, 155.00]
+cash = 10000.00
+shares = 0
+step_num = 0
+
+class Action(BaseModel):
+    action: str
+    amount: Optional[int] = 0
+
+@app.get("/health")
+def health():
+    return {"status": "healthy"}
+
+@app.get("/")
+def root():
+    return {"message": "Trading API Running"}
+
+@app.post("/reset")
+def reset():
+    global cash, shares, step_num
+    cash = 10000.00
+    shares = 0
+    step_num = 0
+    return {"cash": cash, "shares": shares, "price": prices[0]}
+
+@app.post("/step")
+def step(action: Action):
+    global cash, shares, step_num
+    step_num = min(step_num + 1, len(prices) - 1)
+    price = prices[step_num]
+    
+    if action.action == "BUY" and action.amount:
+        cost = price * action.amount
+        if cost <= cash:
+            cash -= cost
+            shares += action.amount
+    elif action.action == "SELL" and action.amount:
+        if action.amount <= shares:
+            cash += price * action.amount
+            shares -= action.amount
+    
+    return {
+        "price": price,
+        "cash": cash,
+        "shares": shares,
+        "portfolio_value": cash + (shares * price)
+    }
+
+@app.get("/tasks")
+def tasks():
+    return {
+        "tasks": [
+            {"id": 1, "name": "Get Price"},
+            {"id": 2, "name": "Buy Stock"},
+            {"id": 3, "name": "Sell Stock"}
+        ]
+    }

server/data/download_data.py

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+import yfinance as yf
+import pandas as pd
+import os
+
+print(" Downloading fresh AAPL data...")
+df = yf.download("AAPL", period="1mo", interval="1h", progress=False)
+
+# Clean up the data
+df = df.reset_index()
+df.columns = ['datetime', 'open', 'high', 'low', 'close', 'volume']
+
+# Convert datetime to string
+df['datetime'] = df['datetime'].dt.strftime('%Y-%m-%d %H:%M:%S')
+
+# Save to CSV (create directory if needed)
+os.makedirs("server/data", exist_ok=True)
+df.to_csv("server/data/prices.csv", index=False)
+print(f"Saved {len(df)} rows of clean data")
+print("\nFirst 3 rows:")
+print(df[['datetime', 'close']].head(3))
+print("\nLast 3 rows:")
+print(df[['datetime', 'close']].tail(3))

server/data/news.json

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+[
+    {"headline": "Apple announces breakthrough AI chip for next iPhone", "sentiment": "positive", "source": "TechCrunch"},
+    {"headline": "Supply chain delays may impact Q2 production", "sentiment": "negative", "source": "Bloomberg"},
+    {"headline": "Analysts raise AAPL price target to $250", "sentiment": "positive", "source": "CNBC"},
+    {"headline": "Apple services revenue hits all-time high", "sentiment": "positive", "source": "Reuters"},
+    {"headline": "Competitor launches rival product threatening market share", "sentiment": "negative", "source": "WSJ"}
+]

server/data/prices.csv

@@ -0,0 +1,143 @@
+Price,Close,High,Low,Open,Volume
+Ticker,AAPL,AAPL,AAPL,AAPL,AAPL
+Datetime,,,,,
+2026-03-02 14:30:00+00:00,263.80999755859375,264.5398864746094,260.20001220703125,262.44000244140625,9648357
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server/environment.py

@@ -0,0 +1,66 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+from typing import Optional
+import random
+
+app = FastAPI()
+
+# Simple data
+prices = [150, 152, 151, 153, 155, 154, 156, 158, 157, 159]
+cash = 10000
+shares = 0
+step_num = 0
+
+class Action(BaseModel):
+    action: str  # BUY, SELL, or GET_PRICE
+    amount: Optional[int] = 0
+
+@app.get("/health")
+def health():
+    return {"status": "healthy"}
+
+@app.post("/reset")
+def reset():
+    global cash, shares, step_num
+    cash = 10000
+    shares = 0
+    step_num = 0
+    return {"cash": cash, "shares": shares, "price": prices[0]}
+
+@app.post("/step")
+def step(action: Action):
+    global cash, shares, step_num
+    step_num = min(step_num + 1, len(prices) - 1)
+    price = prices[step_num]
+    
+    if action.action == "BUY" and action.amount:
+        cost = price * action.amount
+        if cost <= cash:
+            cash -= cost
+            shares += action.amount
+    elif action.action == "SELL" and action.amount:
+        if action.amount <= shares:
+            cash += price * action.amount
+            shares -= action.amount
+    
+    return {
+        "price": price,
+        "cash": cash,
+        "shares": shares,
+        "portfolio_value": cash + (shares * price),
+        "step": step_num
+    }
+
+@app.get("/tasks")
+def tasks():
+    return {
+        "tasks": [
+            {"id": 1, "name": "Get Price", "description": "Get current stock price"},
+            {"id": 2, "name": "Buy Stock", "description": "Buy shares of stock"},
+            {"id": 3, "name": "Sell Stock", "description": "Sell shares of stock"}
+        ]
+    }
+
+@app.get("/")
+def root():
+    return {"message": "Trading Environment API", "status": "running"}