Config

app.py

@@ -1,155 +1,83 @@
 import os
-import gradio as gr
-import numpy as np
 import json
-import pandas as pd
+import random
+import gradio as gr
 import matplotlib.pyplot as plt
-import matplotlib
-matplotlib.use('Agg')
-
-# =========================================
-# ======= Environment Core Definition ======
-# =========================================
-
-class TradeArenaEnv_Deterministic:
-    """
-    Odyssey Arena - AI Trading Environment (Deterministic version)
-    """
-    def __init__(self, cfg):
-        self.num_days = cfg["num_days"]
-        self.stocks = cfg["stocks"]
-        self.variables = cfg["variables"]
-        self.dependency_matrix = np.array(cfg["dependency_matrix"])
-        self.initial_prices = np.array(cfg["initial_prices"])
-        self.initial_variables = np.array(cfg["initial_variables"])
-        self.timeline = cfg["timeline"]
-        self.price_noise_scale = cfg.get("price_noise_scale", 0.0)
-        self.initial_cash = cfg.get("initial_cash", 10000.0)
+
+# ======================
+# Environment Definition
+# ======================
+class MarketEnv:
+    def __init__(self, config):
+        self.config = config
+        self.stocks = config.get("stocks", ["AAPL", "GOOG", "TSLA", "AMZN"])
+        self.num_days = config.get("num_days", 30)
         self.reset()
 
     def reset(self):
-        self.t = 0
-        self.cash = self.initial_cash
-        self.positions = np.zeros(len(self.stocks), dtype=np.float64)
-        self.prices = self.initial_prices.copy().astype(np.float64)
-        self.variables_state = self.initial_variables.copy().astype(np.float64)
-        self.next_day_news = self.timeline.get("day_1", None)
-        return self._get_observation()
-
-    def _get_observation(self):
-        obs = {
-            "day": self.t,
-            "prices": {s: float(p) for s, p in zip(self.stocks, self.prices)},
-            "cash": float(self.cash),
-            "positions": {s: int(pos) for s, pos in zip(self.stocks, self.positions)},
-            "total_value": float(self.cash + np.sum(self.positions * self.prices)),
-            "news_next_day": self.next_day_news["variable_changes"] if self.next_day_news else None,
-            "news_next_day_text": self.next_day_news["news_text"] if self.next_day_news else None
-        }
-        return obs
+        self.current_day = 0
+        self.cash = 10000
+        self.portfolio = {s: 0 for s in self.stocks}
+        self.prices = {s: [random.uniform(50, 150)] for s in self.stocks}
+        self.generate_next_day()
+        return self._get_obs()
+
+    def generate_next_day(self):
+        if self.current_day >= self.num_days:
+            return
+        for s in self.stocks:
+            last_price = self.prices[s][-1]
+            change = random.uniform(-0.05, 0.05)
+            new_price = max(1, last_price * (1 + change))
+            self.prices[s].append(new_price)
 
     def step(self, action):
-        assert isinstance(action, dict)
-        
-        # Execute sells first
-        for stock, qty in action.get("sell", {}).items():
-            idx = self.stocks.index(stock)
-            qty = int(qty)
-            qty = min(qty, self.positions[idx])
-            revenue = self.prices[idx] * qty
-            self.positions[idx] -= qty
-            self.cash += revenue
-
-        # Then buys
-        for stock, qty in action.get("buy", {}).items():
-            idx = self.stocks.index(stock)
-            qty = int(qty)
-            cost = self.prices[idx] * qty
-            if cost <= self.cash:
-                self.positions[idx] += qty
-                self.cash -= cost
-
-        # Advance one day
-        self.t += 1
-        done = self.t >= self.num_days
-
-        # Update variable states & prices
-        if not done:
-            news_today = self.timeline.get(f"day_{self.t}", None)
-            if news_today:
-                deltas = np.array(news_today["variable_changes"])
-                self.variables_state += deltas
-                self._update_prices_from_variables(deltas)
-
-        # Prepare next day's news
-        self.next_day_news = self.timeline.get(f"day_{self.t + 1}", None) if not done else None
-
+        self.current_day += 1
+        self.generate_next_day()
+        obs = self._get_obs()
         reward = self._compute_reward()
-        obs = self._get_observation()
+        done = self.current_day >= self.num_days
         return obs, reward, done, {}
 
-    def _update_prices_from_variables(self, delta_vars):
-        delta_price = self.dependency_matrix @ delta_vars
-        noise = np.zeros_like(delta_price) if self.price_noise_scale == 0 else np.random.normal(
-            0, self.price_noise_scale, len(self.stocks)
-        )
-        self.prices += delta_price + noise
-        self.prices = np.clip(self.prices, 0.1, None)
+    def buy(self, stock, amount):
+        price = self.prices[stock][-1]
+        cost = price * amount
+        if self.cash >= cost:
+            self.cash -= cost
+            self.portfolio[stock] += amount
+
+    def sell(self, stock, amount):
+        if self.portfolio[stock] >= amount:
+            price = self.prices[stock][-1]
+            self.cash += price * amount
+            self.portfolio[stock] -= amount
 
     def _compute_reward(self):
-        total_value = self.cash + np.sum(self.positions * self.prices)
-        return round(float(total_value), 2)
-
-
-# =========================================
-# =========== Default Config ==============
-# =========================================
-
-DEFAULT_CONFIG = {
-    "num_days": 30,
-    "stocks": ["TECH", "ENERGY", "FINANCE"],
-    "variables": ["interest_rate", "oil_price", "market_sentiment"],
-    "dependency_matrix": [
-        [-5, 2, 3],
-        [1, 8, 2],
-        [-3, 1, 4]
-    ],
-    "initial_prices": [100, 80, 120],
-    "initial_variables": [0, 0, 0],
-    "initial_cash": 10000,
-    "price_noise_scale": 0,
-    "timeline": {
-        "day_1": {"variable_changes": [0.1, -0.2, 0.3],
-                  "news_text": "Federal Reserve hints at rate increase; Oil prices drop on oversupply concerns"},
-        "day_2": {"variable_changes": [-0.1, 0.3, 0.2],
-                  "news_text": "Tech sector shows strong earnings; Energy stocks rally on production cuts"},
-        "day_3": {"variable_changes": [0.2, 0.1, -0.1],
-                  "news_text": "Market sentiment cautious amid geopolitical tensions"},
-        "day_4": {"variable_changes": [0.0, 0.2, 0.1],
-                  "news_text": "Stable interest rates; Energy sector momentum continues"},
-        "day_5": {"variable_changes": [-0.2, -0.1, 0.0],
-                  "news_text": "Rate cut speculation; Market consolidation"}
-    }
-}
-
-# =========================================
-# =========== Global State ================
-# =========================================
+        total_value = self.cash + sum(self.prices[s][-1] * self.portfolio[s] for s in self.stocks)
+        return total_value
+
+    def _get_obs(self):
+        prices_today = {s: self.prices[s][-1] for s in self.stocks}
+        return {"day": self.current_day, "cash": self.cash, "portfolio": self.portfolio, "prices": prices_today}
+
 
+# ==============
+# Global Objects
+# ==============
 env = None
 history = []
 
 
-# =========================================
-# =========== Utility Functions ===========
-# =========================================
-
+# ==============
+# Config Helpers
+# ==============
 def list_config_files():
     config_dir = "config"
     if not os.path.exists(config_dir):
         return []
     return [f for f in os.listdir(config_dir) if f.endswith(".json")]
 
+
 def load_config_from_file(filename):
     try:
         path = os.path.join("config", filename)
@@ -160,180 +88,114 @@ def load_config_from_file(filename):
         return f"❌ Error reading {filename}: {str(e)}"
 
 
-# =========================================
-# ============ Core Logic =================
-# =========================================
+# ==========================
+# Visualization Helper Tools
+# ==========================
+def create_price_chart():
+    fig, ax = plt.subplots()
+    for s in env.stocks:
+        ax.plot(env.prices[s], label=s)
+    ax.legend()
+    ax.set_title("Stock Prices")
+    ax.set_xlabel("Days")
+    ax.set_ylabel("Price")
+    return fig
+
+
+def create_portfolio_table():
+    table_md = "| Stock | Holdings | Price | Value |\n|:--|:--|:--|:--|\n"
+    for s in env.stocks:
+        price = env.prices[s][-1]
+        qty = env.portfolio[s]
+        table_md += f"| {s} | {qty} | {price:.2f} | {qty * price:.2f} |\n"
+    return table_md
 
-def initialize_env(config_file=None):
+
+# =====================
+# Gradio Event Handlers
+# =====================
+def initialize_env(config_json):
     global env, history
-    
-    if config_file is not None and config_file.strip():
-        try:
-            config = json.loads(config_file)
-        except:
-            return "❌ Invalid JSON file", None, None, None, None, gr.update()
-    else:
-        config = DEFAULT_CONFIG
-    
-    env = TradeArenaEnv_Deterministic(config)
+    try:
+        cfg = json.loads(config_json)
+    except:
+        cfg = {"stocks": ["AAPL", "GOOG", "TSLA", "AMZN"], "num_days": 30}
+
+    env = MarketEnv(cfg)
+    history = []
     obs = env.reset()
-    
-    history = [{
-        'day': obs['day'],
-        'total_value': obs['total_value'],
-        **obs['prices']
-    }]
-    
-    status = f"✅ Session initialized!\n📅 Day: {obs['day']}\n💰 Cash: ${obs['cash']:.2f}\n📊 Total Value: ${obs['total_value']:.2f}"
-    
     return (
-        status,
-        create_portfolio_display(obs),
-        create_news_display(obs),
+        f"✅ Environment initialized with {len(env.stocks)} stocks.",
+        create_portfolio_table(),
         create_price_chart(),
-        create_value_chart(),
         gr.update(choices=env.stocks, value=env.stocks[0])
     )
 
-def create_portfolio_display(obs):
-    data = []
-    for stock in env.stocks:
-        data.append({
-            'Stock': stock,
-            'Price': f"${obs['prices'][stock]:.2f}",
-            'Holdings': obs['positions'][stock],
-            'Value': f"${obs['prices'][stock] * obs['positions'][stock]:.2f}"
-        })
-    return pd.DataFrame(data)
-
-def create_news_display(obs):
-    if obs['news_next_day_text']:
-        news_html = f"""
-        <div style='background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); 
-                    padding: 20px; border-radius: 10px; color: white; margin: 10px 0;'>
-            <h3 style='margin-top: 0;'>📰 Next Day News</h3>
-            <p style='font-size: 16px; line-height: 1.6;'>{obs['news_next_day_text']}</p>
-        """
-        if obs['news_next_day']:
-            news_html += "<p style='font-size: 14px; margin-top: 10px;'><b>Variable Changes:</b><br/>"
-            for i, var in enumerate(env.variables):
-                change = obs['news_next_day'][i]
-                news_html += f"• {var}: <b>{'+' if change > 0 else ''}{change}</b><br/>"
-            news_html += "</p>"
-        news_html += "</div>"
-        return news_html
-    else:
-        return "<div style='padding: 20px; background: #f0f0f0; border-radius: 10px; text-align: center;'>📭 No more news available</div>"
 
-def create_price_chart():
-    if len(history) <= 1:
-        fig, ax = plt.subplots(figsize=(10, 6))
-        ax.text(0.5, 0.5, 'Trade to see price history', ha='center', va='center', fontsize=14, color='gray')
-        ax.axis('off')
-        return fig
-    
-    df = pd.DataFrame(history)
-    fig, ax = plt.subplots(figsize=(10, 6))
-    colors = ['#3b82f6', '#10b981', '#f59e0b', '#ef4444', '#8b5cf6']
-    
-    for i, stock in enumerate(env.stocks):
-        ax.plot(df['day'], df[stock], marker='o', linewidth=2, 
-                color=colors[i % len(colors)], label=stock)
-    
-    ax.set_xlabel('Day', fontsize=12, fontweight='bold')
-    ax.set_ylabel('Price ($)', fontsize=12, fontweight='bold')
-    ax.set_title('Stock Price History', fontsize=14, fontweight='bold', pad=20)
-    ax.legend(loc='best', framealpha=0.9)
-    ax.grid(True, alpha=0.3)
-    ax.set_facecolor('#f8f9fa')
-    fig.patch.set_facecolor('white')
-    plt.tight_layout()
-    return fig
+def act_buy(stock, amount):
+    global env
+    if not env:
+        return "❌ Please initialize environment first.", create_portfolio_table(), create_price_chart()
+    env.buy(stock, int(amount))
+    obs, reward, done, _ = env.step(None)
+    history.append(reward)
+    return (
+        f"✅ Bought {amount} of {stock}",
+        create_portfolio_table(),
+        create_price_chart()
+    )
 
-def create_value_chart():
-    if len(history) <= 1:
-        fig, ax = plt.subplots(figsize=(10, 6))
-        ax.text(0.5, 0.5, 'Trade to see portfolio value', ha='center', va='center', fontsize=14, color='gray')
-        ax.axis('off')
-        return fig
-    
-    df = pd.DataFrame(history)
-    fig, ax = plt.subplots(figsize=(10, 6))
-    ax.plot(df['day'], df['total_value'], marker='o', linewidth=3, color='#8b5cf6', label='Portfolio Value')
-    ax.fill_between(df['day'], df['total_value'], alpha=0.2, color='#8b5cf6')
-    initial_value = history[0]['total_value']
-    ax.axhline(y=initial_value, color='red', linestyle='--', alpha=0.5, label=f'Initial: ${initial_value:.2f}')
-    ax.legend(loc='best', framealpha=0.9)
-    ax.set_xlabel('Day', fontsize=12, fontweight='bold')
-    ax.set_ylabel('Total Value ($)', fontsize=12, fontweight='bold')
-    ax.set_title('Portfolio Value Over Time', fontsize=14, fontweight='bold', pad=20)
-    ax.grid(True, alpha=0.3)
-    plt.tight_layout()
-    return fig
+
+def act_sell(stock, amount):
+    global env
+    if not env:
+        return "❌ Please initialize environment first.", create_portfolio_table(), create_price_chart()
+    env.sell(stock, int(amount))
+    obs, reward, done, _ = env.step(None)
+    history.append(reward)
+    return (
+        f"✅ Sold {amount} of {stock}",
+        create_portfolio_table(),
+        create_price_chart()
+    )
 
 
-# =========================================
-# ============ UI Definition ==============
-# =========================================
+# ==============
+# Gradio UI
+# ==============
+with gr.Blocks(theme=gr.themes.Soft(), title="AI Trading Arena") as demo:
+    gr.Markdown("# 💹 AI Trading Arena")
+    status_output = gr.Markdown("👋 Ready to start your trading journey!")
 
-custom_css = """
-.gradio-container { font-family: 'Arial', sans-serif; }
-.gr-button-primary {
-    background: linear-gradient(90deg, #667eea 0%, #764ba2 100%) !important;
-    border: none !important;
-}
-.gr-button-secondary {
-    background: linear-gradient(90deg, #f093fb 0%, #f5576c 100%) !important;
-    border: none !important;
-}
-"""
+    with gr.Accordion("📁 Configuration", open=False):
+        config_file_dropdown = gr.Dropdown(
+            choices=list_config_files(),
+            label="Choose Config File",
+            value=list_config_files()[0] if list_config_files() else None
+        )
+        load_file_btn = gr.Button("📂 Load from File", variant="secondary")
+        config_input = gr.Textbox(label="Custom Config JSON", lines=4)
+        init_btn = gr.Button("🚀 Initialize Environment", variant="primary")
 
-with gr.Blocks(theme=gr.themes.Soft(), css=custom_css, title="AI Trading Arena") as demo:
-    gr.Markdown("# 🚀 AI Trading Arena\n### Interactive Stock Trading Simulator")
+    portfolio_table = gr.Markdown()
+    price_chart = gr.Plot()
 
     with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown("## 🎮 Control Panel")
-
-            with gr.Accordion("📁 Configuration", open=False):
-                config_file_dropdown = gr.Dropdown(
-                    choices=list_config_files(),
-                    label="Choose Config File from /config",
-                    value=list_config_files()[0] if list_config_files() else None
-                )
-                load_file_btn = gr.Button("📂 Load from File", variant="secondary")
-                config_input = gr.Textbox(
-                    label="Custom Config JSON",
-                    placeholder='{"num_days": 30, "stocks": ["TECH", "ENERGY"], ...}',
-                    lines=4
-                )
-                init_btn = gr.Button("🚀 Initialize Environment", variant="primary", size="lg")
-
-            with gr.Accordion("💹 Trading Operations", open=True):
-                stock_dropdown = gr.Dropdown(choices=DEFAULT_CONFIG["stocks"], label="Select Stock", value=DEFAULT_CONFIG["stocks"][0])
-                action_radio = gr.Radio(choices=["Buy", "Sell"], label="Action", value="Buy")
-                amount_input = gr.Number(label="Amount (shares)", value=10, minimum=1, step=1)
-                trade_btn = gr.Button("📈 Execute Trade", variant="primary", size="lg")
-
-            with gr.Row():
-                advance_btn = gr.Button("⏭️ Next Day", variant="primary", size="lg")
-                reset_btn = gr.Button("🔄 Reset", variant="secondary", size="lg")
-
-            status_output = gr.Textbox(label="📊 Status & Messages", lines=8, interactive=False)
-
-        with gr.Column(scale=2):
-            gr.Markdown("## 📊 Market Dashboard")
-            portfolio_table = gr.Dataframe(label="💼 Portfolio Holdings", interactive=False)
-            news_display = gr.HTML(label="📰 Market News")
-            with gr.Tab("📈 Price History"): price_chart = gr.Plot(label="Stock Prices Over Time")
-            with gr.Tab("💰 Portfolio Value"): value_chart = gr.Plot(label="Total Portfolio Value")
-
-    # === Button Bindings ===
-    load_file_btn.click(fn=load_config_from_file, inputs=[config_file_dropdown], outputs=[config_input])
-    init_btn.click(fn=initialize_env, inputs=[config_input],
-                   outputs=[status_output, portfolio_table, news_display, price_chart, value_chart, stock_dropdown])
-
-    demo.load(fn=initialize_env, inputs=[], outputs=[status_output, portfolio_table, news_display, price_chart, value_chart, stock_dropdown])
-
-if __name__ == "__main__":
-    demo.launch()
+        stock_dropdown = gr.Dropdown(choices=["AAPL", "GOOG"], label="Stock")
+        amount_slider = gr.Slider(1, 10, step=1, label="Amount")
+    with gr.Row():
+        buy_btn = gr.Button("🟢 Buy")
+        sell_btn = gr.Button("🔴 Sell")
+
+    load_file_btn.click(load_config_from_file, [config_file_dropdown], [config_input])
+
+    init_btn.click(
+        initialize_env,
+        inputs=[config_input],
+        outputs=[status_output, portfolio_table, price_chart, stock_dropdown]
+    )
+
+    buy_btn.click(act_buy, [stock_dropdown, amount_slider], [status_output, portfolio_table, price_chart])
+    sell_btn.click(act_sell, [stock_dropdown, amount_slider], [status_output, portfolio_table, price_chart])
+
+demo.launch()