Create finrl_simple.py

Author: init27

examples/finrl_simple.py

@@ -0,0 +1,152 @@
+#!/usr/bin/env python3
+"""
+Simple example showing how to use the FinRL environment with OpenEnv.
+
+This example demonstrates:
+1. Connecting to a FinRL environment server
+2. Resetting the environment
+3. Executing random trading actions
+4. Tracking portfolio value over time
+"""
+
+import sys
+from pathlib import Path
+
+import numpy as np
+
+# Add src to path
+sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
+
+from envs.finrl_env import FinRLAction, FinRLEnv
+
+
+def main():
+    """Run a simple FinRL environment example."""
+    print("=" * 70)
+    print("FinRL Environment - Simple Example")
+    print("=" * 70)
+    print()
+
+    # Connect to server
+    print("Connecting to FinRL environment at http://localhost:8000...")
+    try:
+        client = FinRLEnv(base_url="http://localhost:8000")
+    except Exception as e:
+        print(f"❌ Failed to connect to server: {e}")
+        print()
+        print("Make sure the server is running:")
+        print("  docker run -p 8000:8000 finrl-env:latest")
+        return False
+
+    print("✅ Connected successfully!")
+    print()
+
+    # Get configuration
+    try:
+        config = client.get_config()
+        print("Environment Configuration:")
+        print(f"  Stock dimension: {config['stock_dim']}")
+        print(f"  Initial amount: ${config['initial_amount']:,.0f}")
+        print(f"  Action space: {config['action_space']}")
+        print(f"  State space: {config['state_space']}")
+        print(f"  Technical indicators: {', '.join(config['tech_indicators'])}")
+        print()
+    except Exception as e:
+        print(f"⚠️  Could not fetch config: {e}")
+        print()
+
+    # Reset environment
+    print("Resetting environment...")
+    result = client.reset()
+    print(f"✅ Environment reset successfully!")
+    print(f"   Initial portfolio value: ${result.observation.portfolio_value:,.2f}")
+    print(f"   State dimension: {len(result.observation.state)}")
+    if result.observation.date:
+        print(f"   Starting date: {result.observation.date}")
+    print()
+
+    # Run trading simulation
+    print("-" * 70)
+    print("Running 20-step trading simulation with random actions...")
+    print("-" * 70)
+    print()
+
+    portfolio_history = [result.observation.portfolio_value]
+    cumulative_reward = 0
+
+    for step in range(20):
+        # Get current state
+        state = result.observation.state
+
+        # Generate random actions (in real use, replace with your RL policy)
+        num_stocks = config.get("stock_dim", 1)
+        actions = np.random.uniform(-0.5, 0.5, size=num_stocks).tolist()
+
+        # Execute action
+        result = client.step(FinRLAction(actions=actions))
+
+        # Track metrics
+        portfolio_history.append(result.observation.portfolio_value)
+        cumulative_reward += result.reward or 0
+
+        # Print progress
+        print(
+            f"Step {step + 1:2d}: "
+            f"Portfolio=${result.observation.portfolio_value:>12,.2f} | "
+            f"Reward={result.reward:>8.2f} | "
+            f"Date={result.observation.date}"
+        )
+
+        if result.done:
+            print()
+            print("Episode finished!")
+            break
+
+    # Summary
+    print()
+    print("-" * 70)
+    print("Trading Simulation Complete")
+    print("-" * 70)
+    print(f"Initial portfolio value: ${portfolio_history[0]:,.2f}")
+    print(f"Final portfolio value:   ${portfolio_history[-1]:,.2f}")
+    print(
+        f"Total return:            ${portfolio_history[-1] - portfolio_history[0]:,.2f} "
+        f"({((portfolio_history[-1] / portfolio_history[0] - 1) * 100):.2f}%)"
+    )
+    print(f"Cumulative reward:       {cumulative_reward:.2f}")
+    print(f"Steps executed:          {len(portfolio_history) - 1}")
+    print()
+
+    # Plot portfolio value over time (if matplotlib available)
+    try:
+        import matplotlib.pyplot as plt
+
+        plt.figure(figsize=(10, 6))
+        plt.plot(portfolio_history, marker="o", linewidth=2)
+        plt.title("Portfolio Value Over Time", fontsize=14, fontweight="bold")
+        plt.xlabel("Step", fontsize=12)
+        plt.ylabel("Portfolio Value ($)", fontsize=12)
+        plt.grid(True, alpha=0.3)
+        plt.tight_layout()
+        plt.savefig("finrl_portfolio_history.png", dpi=150)
+        print("📊 Portfolio chart saved to: finrl_portfolio_history.png")
+        print()
+    except ImportError:
+        pass
+
+    # Cleanup
+    print("Closing connection...")
+    client.close()
+    print("✅ Done!")
+    print()
+
+    print("=" * 70)
+    print("Example completed successfully! 🎉")
+    print("=" * 70)
+
+    return True
+
+
+if __name__ == "__main__":
+    success = main()
+    sys.exit(0 if success else 1)