Update app.py

app.py

@@ -1,11 +1,3 @@
-# app.py — Bullish Minds AI: Stock Research + Trading Curriculum
-# Run: pip install gradio requests numpy pandas feedparser
-# Launch: python app.py
-# Notes:
-# - Polygon on free plan: previous close endpoint used.
-# - Finnhub metrics endpoint requires token.
-# - SEC requires descriptive User-Agent.
-
 import gradio as gr
 import requests
 import numpy as np
@@ -16,15 +8,11 @@ import random
 from datetime import datetime
 import os
 
-# =========================
-# API Keys (ENV preferred)
-# =========================
+# === CONFIG: API KEYS ===
 POLYGON_API_KEY = os.getenv("POLYGON_API_KEY") or "fAhg47wPlf4FT6U2Hn23kQoQCQIyW0G_"
-FINNHUB_API_KEY = os.getenv("FINNHUB_API_KEY") or "d2urs69r01qq994h1f5gd2urs69r01qq994h1f60"
+FINNHUB_API_KEY = "d2urs69r01qq994h1f5gd2urs69r01qq994h1f60"
 
-# =========================
-# Data Fetchers
-# =========================
+# === 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:
@@ -32,89 +20,84 @@ def fetch_polygon_quote(ticker, polygon_api_key=POLYGON_API_KEY):
         response.raise_for_status()
         data = response.json()
         if data.get("results"):
-            last = data["results"]
-            price = last.get("c")
-            ts = last.get("t")
-            if price is None or ts is None:
-                return f"❌ Quote format unexpected for {ticker.upper()}."
-            close_dt = datetime.utcfromtimestamp(ts / 1000).strftime('%Y-%m-%d')
-            return f"💰 **Previous Close for {ticker.upper()} (as of {close_dt})**\n\n• **Close Price:** ${price:.2f}\n\n_(Free Polygon plan provides prior close)_"
+            last = data["results"][0]
+            price = last["c"]
+            close_dt = datetime.utcfromtimestamp(last["t"] / 1000).strftime('%Y-%m-%d')
+            return f"💰 **Previous Close for {ticker.upper()} (as of {close_dt})**\n\n• **Close Price:** ${price:.2f}\n\n_(Free Polygon plan only provides prior close)_"
         else:
             return f"❌ Quote data unavailable for {ticker.upper()}."
     except Exception as e:
         return f"❌ Error: {str(e)}"
 
+# === FINNHUB FINANCIAL SUMMARY ===
 def get_financial_summary_finnhub(ticker, finnhub_api_key=FINNHUB_API_KEY):
     url = f"https://finnhub.io/api/v1/stock/metric?symbol={ticker.upper()}&metric=all&token={finnhub_api_key}"
     try:
         response = requests.get(url, timeout=10)
         response.raise_for_status()
-        data = response.json() or {}
-        metrics = data.get('metric', {}) or {}
+        data = response.json()
+        metrics = data.get('metric', {})
         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"
-        if metrics.get('totalRevenueTTM') is not None:
+        if metrics.get('totalRevenueTTM'):
             result += f"• **Revenue (TTM):** ${int(metrics['totalRevenueTTM']):,}\n"
-        if metrics.get('netIncomeTTM') is not None:
+        if metrics.get('netIncomeTTM'):
             result += f"• **Net Income (TTM):** ${int(metrics['netIncomeTTM']):,}\n"
         pe = metrics.get('peNormalizedAnnual') or metrics.get('peExclExtraTTM')
         if pe is not None:
-            result += f"• **P/E Ratio:** {float(pe):.2f}\n"
+            result += f"• **P/E Ratio:** {pe:.2f}\n"
         pb = metrics.get('pbAnnual')
         if pb is not None:
-            result += f"• **P/B Ratio:** {float(pb):.2f}\n"
+            result += f"• **P/B Ratio:** {pb:.2f}\n"
         dy = metrics.get('dividendYieldIndicatedAnnual')
         if dy is not None:
-            result += f"• **Dividend Yield:** {float(dy):.2f}%\n"
+            result += f"• **Dividend Yield:** {dy:.2f}%\n"
         dte = metrics.get('totalDebt/totalEquityAnnual')
         if dte is not None:
-            result += f"• **Debt/Equity:** {float(dte):.2f}\n"
+            result += f"• **Debt/Equity:** {dte:.2f}\n"
         pm = metrics.get('netProfitMarginTTM')
         if pm is not None:
-            result += f"• **Net Profit Margin:** {float(pm):.2f}%\n"
+            result += f"• **Net Profit Margin:** {pm:.2f}%\n"
         mc = metrics.get('marketCapitalization')
         if mc is not None:
             result += f"• **Market Cap:** ${int(mc):,}\n"
         if result.strip() == f"📊 **Financial Summary for {ticker.upper()}**":
-            return f"📊 **Financial Summary for {ticker.upper()}**\n\n❌ No data available."
+            return f"📊 **Financial Summary for {ticker.upper()}**\n\n❌ No data available from Finnhub."
         return result
     except Exception as e:
         return f"📊 **Financial Summary for {ticker.upper()}**\n\n❌ Error fetching financial summary: {e}"
 
-# =========================
-# SEC Utilities
-# =========================
+# === SEC Utilities ===
 class SECUtils:
     def __init__(self):
         self.cik_lookup_url = "https://www.sec.gov/files/company_tickers.json"
         self.edgar_search_url = "https://data.sec.gov/submissions/CIK{cik}.json"
-        self.headers = {"User-Agent": "BullishMindsAI/1.0 (marcus@bullishmindsai.co.site)"}
-
+        self.headers = {"User-Agent": "StockResearchMVP/1.0 (educational@example.com)"}
     def get_cik(self, ticker):
         try:
             time.sleep(0.5)
             response = requests.get(self.cik_lookup_url, headers=self.headers, timeout=20)
-            response.raise_for_status()
+            if response.status_code != 200:
+                return None
             data = response.json()
-            for _, v in data.items():
+            for k, v in data.items():
                 if isinstance(v, dict) and v.get('ticker', '').upper() == ticker.upper():
                     return str(v['cik_str']).zfill(10)
             return None
         except Exception as e:
             print(f"CIK lookup error: {e}")
             return None
-
     def get_recent_filings(self, ticker):
         try:
             cik = self.get_cik(ticker)
             if not cik:
-                return f"📄 **SEC Filings for {ticker}**\n\n❌ CIK not found. May be a newer ticker.\n\n💡 Try SEC EDGAR search."
+                return f"📄 **SEC Filings for {ticker}**\n\n❌ CIK not found. This may be a newer company or ticker not in SEC database.\n\n💡 Try checking [SEC EDGAR directly](https://www.sec.gov/edgar/search/) for this company."
             time.sleep(0.5)
             url = self.edgar_search_url.format(cik=cik)
             response = requests.get(url, headers=self.headers, timeout=20)
             if response.status_code != 200:
-                return f"📄 **SEC Filings for {ticker}**\n\n❌ Unable to fetch SEC data (Status: {response.status_code})."
+                return f"📄 **SEC Filings for {ticker}**\n\n❌ Unable to fetch SEC data (Status: {response.status_code}).\n\n💡 Try [SEC EDGAR search](https://www.sec.gov/edgar/search/) directly."
             data = response.json()
             filings = data.get('filings', {}).get('recent', {})
             if not filings or not filings.get('form'):
@@ -133,22 +116,19 @@ class SECUtils:
                     result += f" 📎 [View Filing]({filing_url})\n\n"
             return result
         except Exception as e:
-            return f"📄 **SEC Filings for {ticker}**\n\n❌ Error fetching SEC filings: {str(e)}"
+            return f"📄 **SEC Filings for {ticker}**\n\n❌ Error fetching SEC filings: {str(e)}\n\n💡 Try [SEC EDGAR search](https://www.sec.gov/edgar/search/) directly."
 
-# =========================
-# News Utilities (Yahoo RSS)
-# =========================
+# === News Utilities ===
 class NewsUtils:
     def __init__(self):
-        self.headers = {"User-Agent": "BullishMindsAI/1.0 (marcus@bullishmindsai.co.site)"}
-
+        self.headers = {"User-Agent": "StockResearchMVP/1.0 (educational@example.com)"}
     def get_yahoo_news(self, ticker):
         try:
             time.sleep(random.uniform(0.5, 1.0))
             url = f"https://feeds.finance.yahoo.com/rss/2.0/headline?s={ticker}&region=US&lang=en-US"
             feed = feedparser.parse(url)
             if not feed.entries:
-                return f"📰 **Latest News for {ticker}**\n\n❌ No recent news found via RSS feed."
+                return f"📰 **Latest News for {ticker}**\n\n❌ No recent news found via RSS feed.\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})"
             result = f"📰 **Latest News for {ticker}**\n\n"
             for i, entry in enumerate(feed.entries[:5]):
                 title = getattr(entry, 'title', 'No title')
@@ -159,32 +139,35 @@ class NewsUtils:
                 result += f" 🔗 [Read More]({link})\n\n"
             return result
         except Exception as e:
-            return f"📰 **Latest News for {ticker}**\n\n❌ Error fetching news: {str(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})"
 
-# =========================
-# Chart Embed (TradingView)
-# =========================
+# === TradingView Widget Chart Embed ===
 def get_tradingview_embed(ticker):
-    ticker = (ticker or "AAPL").strip().upper()
+    ticker = ticker.strip().upper() if ticker else "AAPL"
     ticker = ''.join(filter(str.isalnum, ticker))
-    return f'<iframe src="https://s.tradingview.com/widgetembed/?symbol={ticker}&interval=D&hidesidetoolbar=1&theme=light" width="100%" height="400" frameborder="0" allowtransparency="true" scrolling="no"></iframe>'
+    return f"""
+<iframe src="https://s.tradingview.com/widgetembed/?symbol={ticker}&interval=D&hidesidetoolbar=1&theme=light"
+        width="100%" height="400" frameborder="0" allowtransparency="true" scrolling="no"></iframe>
+"""
+
+# === LESSON MODULES ===
 
-# =========================
-# Simulators/Calculators (Existing)
-# =========================
+# 1. Order Book Simulator
 def simulate_order_book(side, order_type, price, size, seed=123):
-    np.random.seed(int(seed) if seed is not None else 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)
+    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":
@@ -200,8 +183,8 @@ def simulate_order_book(side, order_type, price, size, seed=123):
         if side == "Buy":
             if price >= df['Price'].min():
                 sells_at_or_below = df[(df['Price'] <= price) & (df['Sell Size'] > 0)]
-                if not sells_at_or_below.empty:
-                    fill_price = sells_at_or_below.iloc['Price']
+                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)
@@ -211,8 +194,8 @@ def simulate_order_book(side, order_type, price, size, seed=123):
         else:
             if price <= df['Price'].max():
                 buys_at_or_above = df[(df['Price'] >= price) & (df['Buy Size'] > 0)]
-                if not buys_at_or_above.empty:
-                    fill_price = buys_at_or_above.iloc['Price']
+                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)
@@ -221,91 +204,37 @@ def simulate_order_book(side, order_type, price, size, seed=123):
                 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(int(seed) if seed is not None else 123)
+    np.random.seed(seed)
     base_price = 100
-    levels = np.arange(base_price - 2, base_price + 2.5, 0.5)
-
+    levels = np.arange(base_price-2, base_price+2.5, 0.5)
     if side == "Buy":
-        sizes_all = np.random.randint(10, 70, len(levels))
-        mask = levels > base_price
-        prices = levels[mask]
-        sizes = sizes_all[mask]
+        sizes = np.random.randint(10, 70, len(levels))
+        prices = levels[levels > base_price]
+        sizes = sizes[levels > base_price]
     else:
-        sizes_all = np.random.randint(10, 70, len(levels))
-        mask = levels < base_price
-        prices = levels[mask]
-        sizes = sizes_all[mask]
-
-    remaining = int(order_size)
-    if remaining <= 0:
-        return "Order size must be > 0", None
-
+        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(int(s), remaining)
-        if take > 0:
-            fills.append((p, take))
-            remaining -= take
+        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() / df["Shares"].sum()
+    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
 
-# =========================
-# New Curriculum Calculators
-# =========================
-def rr_position_size_calc(account_equity: float, risk_pct: float, entry: float, stop: float) -> str:
-    if account_equity <= 0 or risk_pct <= 0:
-        return "Inputs must be positive."
-    risk_dollars = account_equity * (risk_pct / 100.0)
-    per_share_risk = max(1e-6, abs(entry - stop))
-    shares = int(risk_dollars // per_share_risk)
-    rr2_target = entry + 2 * (entry - stop) if entry > stop else entry - 2 * (stop - entry)
-    rr3_target = entry + 3 * (entry - stop) if entry > stop else entry - 3 * (stop - entry)
-    return (
-        f"Risk: ${risk_dollars:,.2f}\n"
-        f"Max shares: {shares:,}\n"
-        f"Targets: 2R={rr2_target:.2f}, 3R={rr3_target:.2f}"
-    )
-
-def atr_stop_calc(entry: float, atr: float, atr_mult: float, direction: str) -> str:
-    if atr <= 0 or atr_mult <= 0:
-        return "ATR and multiplier must be > 0."
-    if direction == "Long":
-        stop = entry - atr_mult * atr
-    else:
-        stop = entry + atr_mult * atr
-    return f"Suggested stop: {stop:.2f}"
-
-def expectancy_calc(win_rate_pct: float, avg_win: float, avg_loss: float) -> str:
-    p = max(0.0, min(1.0, win_rate_pct / 100.0))
-    if avg_win < 0 or avg_loss <= 0:
-        return "Avg win must be >= 0 and avg loss > 0."
-    exp = p * avg_win - (1 - p) * avg_loss
-    return f"Expectancy per trade: {exp:.2f}"
-
-def risk_of_ruin_estimator(win_rate_pct: float, reward_risk: float, bankroll_risk_pct: float) -> str:
-    p = max(0.0, min(1.0, win_rate_pct / 100.0))
-    r = max(1e-6, reward_risk)
-    b = max(1e-6, bankroll_risk_pct / 100.0)
-    edge = p * r - (1 - p)
-    if edge <= 0:
-        return "High risk of ruin (negative/zero edge)."
-    approx_ror = max(0.0, min(1.0, (1 - edge) ** (1 / b)))
-    return f"Heuristic risk of ruin: {approx_ror*100:.1f}% (educational)"
-
-# =========================
-# Orchestration
-# =========================
+# === Instantiate Utilities ===
 sec_utils = SECUtils()
 news_utils = NewsUtils()
 
@@ -325,24 +254,27 @@ def update_stock_info(ticker):
     chart_html = get_tradingview_embed(ticker)
     return quote_data, news_data, filings_data, financial_data, chart_html
 
-# =========================
-# UI
-# =========================
 css = """
-.gradio-container {font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; max-width: 1400px; margin: 0 auto;}
-.tab-nav button {font-size: 16px; font-weight: 600;}
+.gradio-container {
+ font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+ max-width: 1400px;
+ margin: 0 auto;
+}
+.tab-nav button {
+ font-size: 16px;
+ font-weight: 600;
+}
 """
 
-with gr.Blocks(css=css, theme=gr.themes.Soft(), title="Bullish Minds AI - Stock Research & Education") as demo:
-    if os.path.exists("logo.png"):
-        gr.Image("logo.png", elem_id="header-logo", show_label=False, show_download_button=False)
+with gr.Blocks(css=css, theme=gr.themes.Soft(), title="Stock Research Platform") as demo:
     gr.Markdown("""
-# **Bullish Minds AI**
-Stock Research Platform + Trading Curriculum
+# 📈 Stock Research Platform MVP
+**Comprehensive stock analysis, real-time data, and interactive education modules.**
 
-Honest, educational, and data‑rich. Use quotes, news, filings, and charts alongside interactive lessons. Not financial advice.
-""")
+🎯 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 from Polygon (Previous Close for free plans). Financial summary from Finnhub. Charts powered by TradingView.
+""")
     with gr.Row():
         with gr.Column(scale=3):
             ticker_input = gr.Textbox(
@@ -351,10 +283,8 @@ Honest, educational, and data‑rich. Use quotes, news, filings, and charts alon
                 value="AAPL"
             )
         with gr.Column(scale=1):
-            refresh_btn = gr.Button("🔄 Refresh Data", variant="primary")
-
+            refresh_btn = gr.Button("🔄 Refresh Data", variant="primary", size="lg")
     with gr.Tabs():
-        # ========== Research Tabs ==========
         with gr.TabItem("💰 Quote & Overview"):
             quote_output = gr.Markdown(value="Enter a ticker to see stock quote")
         with gr.TabItem("📰 News"):
@@ -367,304 +297,60 @@ Honest, educational, and data‑rich. Use quotes, news, filings, and charts alon
             gr.Markdown("### Interactive Price Chart")
             gr.Markdown("*Powered by TradingView*")
             chart_output = gr.HTML(get_tradingview_embed("AAPL"))
-
-        # ========== Education Tabs ==========
-        with gr.TabItem("🎓 Education"):
+        with gr.TabItem("🎓 Lessons"):
             with gr.Tabs():
-                # Orientation
-                with gr.TabItem("0) Orientation"):
+                with gr.TabItem("Lesson 1: Exchanges & Order Book"):
                     gr.Markdown("""
-## Orientation & Disclaimers
-- Educational purpose only; not financial advice. Trading involves risk of loss of principal.  
-- Suggested paths: Beginner → Foundations + Risk/Psych → Day or Swing → Validation → Compliance; Long‑Term path for investors.  
-- Time guide: Foundations (3–5 hrs), Risk/Psych (2–3 hrs), Track (6–12 hrs), Validation/Compliance (3–5 hrs), Capstones (varies).  
-""")
-                    gr.Markdown("### How to use")
-                    gr.Markdown("- Read lessons, use calculators/simulators, complete quizzes and assignments. Journal and review weekly.")
-
-                # Foundations
-                with gr.TabItem("1) Foundations"):
-                    with gr.Tabs():
-                        with gr.TabItem("1.1 Structure & Products"):
-                            gr.Markdown("""
-### Market Structure & Products
-- Exchanges vs dark pools; auction vs dealer markets; where liquidity lives.  
-- Tickers, float, market cap, sectors, indices; why breadth/rotation matter.  
-- Products: stocks, ETFs, ADRs; options/futures context only at this stage.  
-""")
-                            gr.Markdown("#### Interactive: Order Book + Slippage")
-                            gr.Markdown("- Use the Order Book Simulator and Slippage Estimator below to see fill behavior.")
-                            with gr.Row():
-                                with gr.Column():
-                                    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")
-                                        ],
-                                        outputs=[gr.Dataframe(), gr.Textbox(label="Fill Message")],
-                                        live=False,
-                                        allow_flagging="never"
-                                    )
-                                with gr.Column():
-                                    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")
-                                        ],
-                                        outputs=[gr.Textbox(label="Estimate"), gr.Dataframe()],
-                                        live=False,
-                                        allow_flagging="never"
-                                    )
-                            gr.Markdown("#### Quiz")
-                            q1 = gr.Radio(
-                                ["Exchanges and dark pools route orders differently", "Dark pools set the NBBO", "Dealer markets have no market makers"],
-                                label="Which statement is true?"
-                            )
-                            q1_out = gr.Markdown()
-                            def foundations_q1(ans):
-                                return "Correct." if ans == "Exchanges and dark pools route orders differently" else "Review: NBBO set by lit venues; dealers make markets."
-                            q1_btn = gr.Button("Submit")
-                            q1_btn.click(foundations_q1, q1, q1_out)
-
-                        with gr.TabItem("1.2 Accounts & Orders"):
-                            gr.Markdown("""
-### Accounts, Brokers & Order Execution
-- Cash vs margin; PDT basics (US); leverage and borrow fees.  
-- Order types: market, limit, stop, stop‑limit, trailing; OCO and bracket orders for structure.  
-- Slippage, spreads, liquidity; when L2/time & sales help vs harm.  
-""")
-
-                        with gr.TabItem("1.3 Fees & Taxes"):
-                            gr.Markdown("""
-### Fees, Taxes & Recordkeeping
-- Commissions vs PFOF; short borrow fees; margin interest and compounding risk.  
-- High‑level tax: short vs long‑term gains; wash sale basics.  
-- Trade journal: screenshots, tags, metrics for improvement.  
-""")
-
-                        with gr.TabItem("1.4 Charts & Data"):
-                            gr.Markdown("""
-### Charts, Timeframes & Data
-- Candles, OHLC, volume; multi‑timeframe analysis.  
-- Indicators (context): MA, RSI, MACD, ATR, VWAP; know what they measure.  
-- Economic calendar, earnings, splits, dividends; why surprises move price.  
-""")
-
-                # Risk & Psychology
-                with gr.TabItem("2) Risk & Psychology"):
-                    with gr.Tabs():
-                        with gr.TabItem("2.1 Risk Core"):
-                            gr.Markdown("""
-### Risk Management Core
-- Risk‑per‑trade sizing from stop distance (ATR/structure).  
-- Reward:risk, win rate, expectancy; drawdown controls and circuit breakers.  
-""")
-                            with gr.Row():
-                                with gr.Column():
-                                    acct = gr.Number(label="Account Equity ($)", value=5000)
-                                    riskpct = gr.Slider(0.1, 5, value=1.0, step=0.1, label="Risk per Trade (%)")
-                                    entry = gr.Number(label="Entry Price", value=100.0)
-                                    stop = gr.Number(label="Stop Price", value=98.0)
-                                    calc_btn = gr.Button("Position Size & Targets")
-                                with gr.Column():
-                                    rr_out = gr.Textbox(label="Sizing/Targets", lines=6)
-                            calc_btn.click(rr_position_size_calc, [acct, riskpct, entry, stop], rr_out)
-
-                            gr.Markdown("#### Expectancy Calculator")
-                            wr = gr.Slider(10, 90, value=45, step=1, label="Win Rate (%)")
-                            avg_win = gr.Number(label="Avg Win ($)", value=150)
-                            avg_loss = gr.Number(label="Avg Loss ($)", value=100)
-                            exp_btn = gr.Button("Compute Expectancy")
-                            exp_out = gr.Textbox(label="Expectancy", lines=2)
-                            exp_btn.click(expectancy_calc, [wr, avg_win, avg_loss], exp_out)
+## Lesson 1: Exchanges, dark pools, auction vs. dealer markets
 
-                            gr.Markdown("#### Risk of Ruin (Heuristic)")
-                            wr2 = gr.Slider(10, 90, value=45, step=1, label="Win Rate (%)")
-                            rr = gr.Slider(0.5, 3.0, value=1.5, step=0.1, label="Reward:Risk")
-                            bankrisk = gr.Slider(0.5, 10.0, value=1.0, step=0.5, label="Bankroll Risk per Trade (%)")
-                            ror_btn = gr.Button("Estimate")
-                            ror_out = gr.Textbox(label="Risk of Ruin", lines=2)
-                            ror_btn.click(risk_of_ruin_estimator, [wr2, rr, bankrisk], ror_out)
+- *[Paste your main lesson text here, or sync from Google Doc]*
 
-                        with gr.TabItem("2.2 Psychology"):
-                            gr.Markdown("""
-### Trader Psychology
-- Biases: loss aversion, FOMO, recency bias; shape environment to reduce impulse.  
-- Routines: pre/post‑market checklists, journaling; accountability partners.  
-- Written plan + timeouts and trade count limits to curb overtrading.  
-""")
-
-                # Day Trading Track
-                with gr.TabItem("3) Day Trading"):
-                    with gr.Tabs():
-                        with gr.TabItem("3.1 Overview"):
-                            gr.Markdown("""
-### Day Trading Overview
-- Pros: frequent reps/feedback; Cons: noise, slippage, cognitive load.  
-- Capital, PDT, margin; ideal markets (liquid large caps/high RVOL).  
-""")
-                        with gr.TabItem("3.2 Intraday Setups"):
-                            gr.Markdown("""
-### Intraday Setups
-- ORB & pullbacks; VWAP trends/reversions; momentum ignition; mean‑reversion to PDH/PDL; gap‑fills; news/catalyst trades and halts.  
-""")
-                            gr.Markdown("#### ORB/VWAP Practice")
-                            gr.Markdown("- Use paper replay: Define hypothesis/trigger/stop/management on selected intraday charts. Visual simulator coming.")
-                        with gr.TabItem("3.3 Tools & Levels"):
-                            gr.Markdown("""
-### Tools & Levels
-- Pre‑market: gap scan, RVOL, news filter.  
-- Levels: pre‑market H/L, PDH/PDL, weekly pivots; tape reading when it adds edge.  
-""")
-                        with gr.TabItem("3.4 Execution & Risk"):
-                            gr.Markdown("""
-### Execution & Risk
-- Partials, dynamic stops (structure/ATR), hotkeys, brackets, max daily loss, trade count caps.  
-""")
-                        with gr.TabItem("3.5 Playbooks & Cases"):
-                            gr.Markdown("""
-### Playbooks & Case Studies
-- Template: hypothesis, trigger, invalidation, targets, management; A+ examples with metrics and screenshots.  
-""")
-                        with gr.TabItem("3.6 Metrics & Review"):
-                            gr.Markdown("""
-### Metrics & Review
-- KPIs: expectancy, MAE/MFE, avg hold, adherence; weekly scorecard and top‑3 fixes.  
-""")
-
-                # Swing Trading Track
-                with gr.TabItem("4) Swing Trading"):
-                    with gr.Tabs():
-                        with gr.TabItem("4.1 Overview"):
-                            gr.Markdown("""
-### Swing Trading Overview
-- Pros/cons vs day trading; overnight gap risk; higher signal‑to‑noise via daily/weekly frames.  
-""")
-                        with gr.TabItem("4.2 Setups"):
-                            gr.Markdown("""
-### Core Swing Setups
-- Breakouts (base/volatility contraction); pullback to 20/50MA; BOS/higher‑low retests; range trading with ATR stops; earnings season plays.  
-""")
-                        with gr.TabItem("4.3 Scanning"):
-                            gr.Markdown("""
-### Scanning & Watchlists
-- Relative strength/weakness vs sector/index; fundamental overlays: EPS growth, margins, debt, sales acceleration.  
-- Liquidity and ATR filters to right‑size risk.  
-""")
-                        with gr.TabItem("4.4 Entries/Stops/Targets"):
-                            gr.Markdown("""
-### Entries, Stops, and Profit Taking
-- Structural and ATR‑based stops; pyramids/scale; partial profit frameworks; managing gaps/news.  
-""")
-                            with gr.Row():
-                                with gr.Column():
-                                    entry_s = gr.Number(label="Entry", value=50.0)
-                                    atr_s = gr.Number(label="ATR", value=1.5)
-                                    mult_s = gr.Slider(0.5, 5.0, value=2.0, step=0.5, label="ATR Multiplier")
-                                    side_s = gr.Radio(["Long", "Short"], value="Long", label="Direction")
-                                    atr_btn = gr.Button("Compute Stop")
-                                with gr.Column():
-                                    atr_out = gr.Textbox(label="Stop Suggestion", lines=2)
-                            atr_btn.click(atr_stop_calc, [entry_s, atr_s, mult_s, side_s], atr_out)
-                        with gr.TabItem("4.5 Portfolio & Risk"):
-                            gr.Markdown("""
-### Portfolio & Risk
-- Correlation/sector exposure; max names; volatility budgeting; optional options for risk shaping.  
-""")
-                        with gr.TabItem("4.6 Review Cycle"):
-                            gr.Markdown("""
-### Review Cycle
-- Weekly prep (Sunday), mid‑week check‑ins; journaling and refinement.  
-""")
+---
 
-                # Long-Term Investing Track
-                with gr.TabItem("5) Long‑Term"):
-                    with gr.Tabs():
-                        with gr.TabItem("5.1 Foundations"):
-                            gr.Markdown("""
-### Investing Foundations
-- Time horizon and risk capacity vs tolerance; DCA vs lump sum; sequence‑of‑returns risk.  
-""")
-                        with gr.TabItem("5.2 Allocation & Diversification"):
-                            gr.Markdown("""
-### Asset Allocation & Diversification
-- Core indexing (total market + international + bonds); factor tilts (value/small/quality/momentum); rebalancing rules.  
+**Explore:** Try the tools below to visualize real order book mechanics and slippage.
 """)
-                        with gr.TabItem("5.3 Equity Selection (Optional)"):
-                            gr.Markdown("""
-### Equity Selection (Optional)
-- Business quality: moats, ROIC, FCF, balance sheet; valuation snapshots (PE, EV/EBITDA, DCF intuition); dividend growth strategies.  
-""")
-                        with gr.TabItem("5.4 Behavior & Discipline"):
-                            gr.Markdown("""
-### Behavior & Discipline
-- Avoid panic buying/selling; automate contributions; write an IPS (Investment Policy Statement).  
-""")
-                        with gr.TabItem("5.5 Tax Optimization"):
-                            gr.Markdown("""
-### Tax Optimization (High level)
-- Accounts: taxable vs tax‑advantaged; asset location basics; harvest concepts. Consult a tax professional.  
-""")
-
-                # Validation & Development
-                with gr.TabItem("6) Validation"):
                     with gr.Tabs():
-                        with gr.TabItem("6.1 Back/Forward Testing"):
-                            gr.Markdown("""
-### Backtesting & Forward Testing
-- Data quality, survivorship/look‑ahead bias; walk‑forward, out‑of‑sample validation.  
-""")
-                        with gr.TabItem("6.2 Paper → Capital"):
-                            gr.Markdown("""
-### Paper Trading & Phased Deployment
-- Simulator → micro size → scale by adherence/expectancy KPIs.  
-""")
-                        with gr.TabItem("6.3 KPIs & Edge"):
-                            gr.Markdown("""
-### KPIs & Edge Tracking
-- Define edge; maintain playbooks; use an expectancy/KPI dashboard.  
-""")
-
-                # Compliance & Safety
-                with gr.TabItem("7) Compliance"):
-                    gr.Markdown("""
-### Compliance, Ethics & Safety
-- PDT (US) overview; margin basics; short‑selling mechanics and borrow fees; Reg SHO context.  
-- Earnings and MNPI; avoid rumors/pumps; safeguards: max daily loss, time‑outs, risk limits.  
-""")
-
-                # Capstones
-                with gr.TabItem("8) Capstones"):
-                    gr.Markdown("""
-### Capstones & Certifications
-- Day: 20 simulated trades across 3 setups with predefined risk and full journals.  
-- Swing: manage a 5‑name swing portfolio for 6 simulated weeks.  
-- Long‑Term: craft an IPS and backtest a DCA plan through historical drawdowns.  
-""")
+                        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"
+                            )
 
-                # App-Native Components
-                with gr.TabItem("9) App‑Native"):
-                    gr.Markdown("""
-### App‑Native Components
-- Calculators: position size, expectancy, ATR stops, DCA, rebalancing.  
-- Simulators: intraday replay, gap/open auction, earnings reaction.  
-- Checklists: pre‑market, weekly swing review, quarterly IPS.  
-- Dashboards: KPIs (win rate, RR, expectancy, equity curve), risk heatmap.  
-- Journaling: tags, screenshots, reasons to enter/exit, emotions.  
-""")
+                # Add more lessons as new gr.TabItem("Lesson X: ...") blocks here
 
     gr.Markdown("""
 ---
-Data: Polygon (quotes), Finnhub (financials), Yahoo RSS (news), SEC EDGAR (filings), TradingView (charts).
-Troubleshooting: Check tickers, API keys, and retry if rate-limited.
-""")
+**Data Sources:** Polygon.io for quotes, Finnhub for financials, Yahoo RSS for news, SEC EDGAR for filings.
 
+**Troubleshooting:** If you encounter errors, double-check your ticker or wait and retry.
+""")
     ticker_input.change(
         fn=update_stock_info,
         inputs=[ticker_input],
@@ -673,7 +359,7 @@ Troubleshooting: Check tickers, API keys, and retry if rate-limited.
     refresh_btn.click(
         fn=update_stock_info,
         inputs=[ticker_input],
-        outputs=[quote_output, news_output, filings_output, chart_output]
+        outputs=[quote_output, news_output, filings_output, financial_output, chart_output]
     )
 
 if __name__ == "__main__":