Update app.py

app.py

@@ -1,5 +1,7 @@
 import gradio as gr
 import requests
+import numpy as np
+import pandas as pd
 import feedparser
 import time
 import random
@@ -10,7 +12,7 @@ import os
 POLYGON_API_KEY = os.getenv("POLYGON_API_KEY") or "fAhg47wPlf4FT6U2Hn23kQoQCQIyW0G_"
 FINNHUB_API_KEY = "d2urs69r01qq994h1f5gd2urs69r01qq994h1f60"
 
-# === POLYGON QUOTE ===
+# === QUOTE SECTION ===
 def fetch_polygon_quote(ticker, polygon_api_key=POLYGON_API_KEY):
     url = f"https://api.polygon.io/v2/aggs/ticker/{ticker.upper()}/prev?adjusted=true&apiKey={polygon_api_key}"
     try:
@@ -38,7 +40,6 @@ def get_financial_summary_finnhub(ticker, finnhub_api_key=FINNHUB_API_KEY):
         if not metrics:
             return f"📊 **Financial Summary for {ticker.upper()}**\n\n❌ No financial data found."
         result = f"📊 **Financial Summary for {ticker.upper()}**\n\n"
-        # Revenue (TTM)
         if metrics.get('totalRevenueTTM'):
             result += f"• **Revenue (TTM):** ${int(metrics['totalRevenueTTM']):,}\n"
         if metrics.get('netIncomeTTM'):
@@ -140,7 +141,7 @@ class NewsUtils:
         except Exception as e:
             return f"📰 **Latest News for {ticker}**\n\n❌ Error fetching news: {str(e)}\n\n💡 Try these alternatives:\n• [Yahoo Finance News](https://finance.yahoo.com/quote/{ticker}/news)\n• [Google Finance](https://www.google.com/finance/quote/{ticker}:NASDAQ)\n• [MarketWatch](https://www.marketwatch.com/investing/stock/{ticker})"
 
-# === TradingView Widget Embed ===
+# === TradingView Widget Chart Embed ===
 def get_tradingview_embed(ticker):
     ticker = ticker.strip().upper() if ticker else "AAPL"
     ticker = ''.join(filter(str.isalnum, ticker))
@@ -149,7 +150,91 @@ def get_tradingview_embed(ticker):
         width="100%" height="400" frameborder="0" allowtransparency="true" scrolling="no"></iframe>
 """
 
-# === Instantiate utilities ===
+# === LESSON MODULES ===
+
+# 1. Order Book Simulator
+def simulate_order_book(side, order_type, price, size, seed=123):
+    np.random.seed(seed)
+    base_price = 100.00
+    levels = np.arange(base_price - 2, base_price + 2.5, 0.5)
+    buy_sizes = np.random.randint(1, 40, len(levels))
+    sell_sizes = np.random.randint(1, 40, len(levels))
+    buy_mask = levels < base_price
+    sell_mask = levels > base_price
+    buys = np.where(buy_mask, buy_sizes, 0)
+    sells = np.where(sell_mask, sell_sizes, 0)
+    df = pd.DataFrame({
+        'Price': levels,
+        'Buy Size': buys,
+        'Sell Size': sells
+    }).sort_values(by='Price', ascending=False).reset_index(drop=True)
+
+    fill_msg = ""
+    if order_type == "Market":
+        if side == "Buy":
+            best_ask = df.loc[df['Sell Size'] > 0, 'Price'].min()
+            filled = size
+            fill_msg = f"Filled {filled} @ {best_ask:.2f} (Market Buy)"
+        else:
+            best_bid = df.loc[df['Buy Size'] > 0, 'Price'].max()
+            filled = size
+            fill_msg = f"Filled {filled} @ {best_bid:.2f} (Market Sell)"
+    else:
+        if side == "Buy":
+            if price >= df['Price'].min():
+                sells_at_or_below = df[(df['Price'] <= price) & (df['Sell Size'] > 0)]
+                if sells_at_or_below.shape[0]:
+                    fill_price = sells_at_or_below.iloc[0]['Price']
+                    fill_msg = f"Filled {size} @ {fill_price:.2f} (Aggressive Limit Buy)"
+                else:
+                    queue_spot = 1 + np.random.randint(0, 3)
+                    fill_msg = f"Limit buy posted at {price:.2f}. Not immediately filled. Position in queue: #{queue_spot}"
+            else:
+                fill_msg = f"Limit buy posted below book: {price:.2f}. Not filled."
+        else:
+            if price <= df['Price'].max():
+                buys_at_or_above = df[(df['Price'] >= price) & (df['Buy Size'] > 0)]
+                if buys_at_or_above.shape[0]:
+                    fill_price = buys_at_or_above.iloc[0]['Price']
+                    fill_msg = f"Filled {size} @ {fill_price:.2f} (Aggressive Limit Sell)"
+                else:
+                    queue_spot = 1 + np.random.randint(0, 3)
+                    fill_msg = f"Limit sell posted at {price:.2f}. Not immediately filled. Position in queue: #{queue_spot}"
+            else:
+                fill_msg = f"Limit sell posted above book: {price:.2f}. Not filled."
+    return df, fill_msg
+
+# 2. Slippage Estimator
+def slippage_estimator(side, order_size, seed=123):
+    np.random.seed(seed)
+    base_price = 100
+    levels = np.arange(base_price-2, base_price+2.5, 0.5)
+    if side == "Buy":
+        sizes = np.random.randint(10, 70, len(levels))
+        prices = levels[levels > base_price]
+        sizes = sizes[levels > base_price]
+    else:
+        sizes = np.random.randint(10, 70, len(levels))
+        prices = levels[levels < base_price]
+        sizes = sizes[levels < base_price]
+    remaining = order_size
+    fills = []
+    for p, s in zip(prices, sizes):
+        take = min(s, remaining)
+        fills.append((p, take))
+        remaining -= take
+        if remaining <= 0:
+            break
+    if remaining > 0:
+        return "Not enough liquidity to fill order!", None
+    df = pd.DataFrame(fills, columns=["Price", "Shares"])
+    avg_fill = (df["Price"] * df["Shares"]).sum() / order_size
+    slip = avg_fill - base_price if side == "Buy" else base_price - avg_fill
+    slip_pct = (slip / base_price) * 100
+    summary = f"Est. avg fill @ {avg_fill:.2f}; Slippage: {slip:.2f} ({slip_pct:.2f}%) from ideal {base_price}"
+    return summary, df
+
+# === Instantiate Utilities ===
 sec_utils = SECUtils()
 news_utils = NewsUtils()
 
@@ -184,11 +269,11 @@ css = """
 with gr.Blocks(css=css, theme=gr.themes.Soft(), title="Stock Research Platform") as demo:
     gr.Markdown("""
 # 📈 Stock Research Platform MVP
-**Comprehensive stock analysis with real-time data, news, SEC filings, and financial summaries.**
+**Comprehensive stock analysis, real-time data, and interactive education modules.**
 
-🎯 Enter a stock ticker symbol (like **AAPL**, **TSLA**, **MSFT**, **GOOGL**) to explore detailed information.
+🎯 Enter a stock ticker symbol (**AAPL**, **TSLA**, **MSFT**, **GOOGL**) for market data, or check out the Lessons tab for learning modules!
 
-⚠️ **Note**: Stock quote data provided by Polygon (Previous Close for free plans). Financial summary data from Finnhub. News and SEC filings remain free. Charts now powered by TradingView.
+⚠️ **Note**: Stock quote data from Polygon (Previous Close for free plans). Financial summary from Finnhub. Charts powered by TradingView.
 """)
     with gr.Row():
         with gr.Column(scale=3):
@@ -212,6 +297,54 @@ with gr.Blocks(css=css, theme=gr.themes.Soft(), title="Stock Research Platform")
             gr.Markdown("### Interactive Price Chart")
             gr.Markdown("*Powered by TradingView*")
             chart_output = gr.HTML(get_tradingview_embed("AAPL"))
+        with gr.TabItem("🎓 Lessons"):
+            with gr.Tabs():
+                with gr.TabItem("Lesson 1: Exchanges & Order Book"):
+                    gr.Markdown("""
+## Lesson 1: Exchanges, dark pools, auction vs. dealer markets
+
+- *[Paste your main lesson text here, or sync from Google Doc]*
+
+---
+
+**Explore:** Try the tools below to visualize real order book mechanics and slippage.
+""")
+                    with gr.Tabs():
+                        with gr.TabItem("Order Book Simulator"):
+                            lesson1_order = gr.Interface(
+                                fn=simulate_order_book,
+                                inputs=[
+                                    gr.Dropdown(["Buy", "Sell"], label="Order Side"),
+                                    gr.Dropdown(["Market", "Limit"], label="Order Type"),
+                                    gr.Number(value=100.00, label="Order Price (for limit)"),
+                                    gr.Slider(1, 100, value=10, step=1, label="Order Size"),
+                                    gr.Number(value=123, label="Seed (optional, for replay)"),
+                                ],
+                                outputs=[
+                                    gr.Dataframe(label="Order Book (randomized)"),
+                                    gr.Textbox(label="Result / Fill Message"),
+                                ],
+                                live=False,
+                                allow_flagging="never"
+                            )
+                        with gr.TabItem("Slippage Estimator"):
+                            lesson1_slippage = gr.Interface(
+                                fn=slippage_estimator,
+                                inputs=[
+                                    gr.Dropdown(["Buy", "Sell"], label="Order Side"),
+                                    gr.Slider(1, 300, value=50, step=1, label="Order Size"),
+                                    gr.Number(value=123, label="Seed (for repeatability)"),
+                                ],
+                                outputs=[
+                                    gr.Textbox(label="Estimate"),
+                                    gr.Dataframe(label="Fill breakdown"),
+                                ],
+                                live=False,
+                                allow_flagging="never"
+                            )
+
+                # Add more lessons as new gr.TabItem("Lesson X: ...") blocks here
+
     gr.Markdown("""
 ---
 **Data Sources:** Polygon.io for quotes, Finnhub for financials, Yahoo RSS for news, SEC EDGAR for filings.