Update app.py

app.py

@@ -1,3 +1,11 @@
+# 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
@@ -8,9 +16,15 @@ import random
 from datetime import datetime
 import os
 
+# =========================
+# API Keys (ENV preferred)
+# =========================
 POLYGON_API_KEY = os.getenv("POLYGON_API_KEY") or "fAhg47wPlf4FT6U2Hn23kQoQCQIyW0G_"
-FINNHUB_API_KEY = "d2urs69r01qq994h1f5gd2urs69r01qq994h1f60"
+FINNHUB_API_KEY = os.getenv("FINNHUB_API_KEY") or "d2urs69r01qq994h1f5gd2urs69r01qq994h1f60"
 
+# =========================
+# Data Fetchers
+# =========================
 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:
@@ -18,10 +32,13 @@ 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"][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)_"
+            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)_"
         else:
             return f"❌ Quote data unavailable for {ticker.upper()}."
     except Exception as e:
@@ -32,68 +49,72 @@ def get_financial_summary_finnhub(ticker, finnhub_api_key=FINNHUB_API_KEY):
     try:
         response = requests.get(url, timeout=10)
         response.raise_for_status()
-        data = response.json()
-        metrics = data.get('metric', {})
+        data = response.json() or {}
+        metrics = data.get('metric', {}) or {}
         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'):
+        if metrics.get('totalRevenueTTM') is not None:
             result += f"• **Revenue (TTM):** ${int(metrics['totalRevenueTTM']):,}\n"
-        if metrics.get('netIncomeTTM'):
+        if metrics.get('netIncomeTTM') is not None:
             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:** {pe:.2f}\n"
+            result += f"• **P/E Ratio:** {float(pe):.2f}\n"
         pb = metrics.get('pbAnnual')
         if pb is not None:
-            result += f"• **P/B Ratio:** {pb:.2f}\n"
+            result += f"• **P/B Ratio:** {float(pb):.2f}\n"
         dy = metrics.get('dividendYieldIndicatedAnnual')
         if dy is not None:
-            result += f"• **Dividend Yield:** {dy:.2f}%\n"
+            result += f"• **Dividend Yield:** {float(dy):.2f}%\n"
         dte = metrics.get('totalDebt/totalEquityAnnual')
         if dte is not None:
-            result += f"• **Debt/Equity:** {dte:.2f}\n"
+            result += f"• **Debt/Equity:** {float(dte):.2f}\n"
         pm = metrics.get('netProfitMarginTTM')
         if pm is not None:
-            result += f"• **Net Profit Margin:** {pm:.2f}%\n"
+            result += f"• **Net Profit Margin:** {float(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 from Finnhub."
+            return f"📊 **Financial Summary for {ticker.upper()}**\n\n❌ No data available."
         return result
     except Exception as e:
         return f"📊 **Financial Summary for {ticker.upper()}**\n\n❌ Error fetching financial summary: {e}"
 
+# =========================
+# 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": "StockResearchMVP/1.0 (educational@example.com)"}
+        self.headers = {"User-Agent": "BullishMindsAI/1.0 (marcus@bullishmindsai.co.site)"}
+
     def get_cik(self, ticker):
         try:
             time.sleep(0.5)
             response = requests.get(self.cik_lookup_url, headers=self.headers, timeout=20)
-            if response.status_code != 200:
-                return None
+            response.raise_for_status()
             data = response.json()
-            for k, v in data.items():
+            for _, 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. 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."
+                return f"📄 **SEC Filings for {ticker}**\n\n❌ CIK not found. May be a newer ticker.\n\n💡 Try SEC EDGAR search."
             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}).\n\n💡 Try [SEC EDGAR search](https://www.sec.gov/edgar/search/) directly."
+                return f"📄 **SEC Filings for {ticker}**\n\n❌ Unable to fetch SEC data (Status: {response.status_code})."
             data = response.json()
             filings = data.get('filings', {}).get('recent', {})
             if not filings or not filings.get('form'):
@@ -112,18 +133,22 @@ 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)}\n\n💡 Try [SEC EDGAR search](https://www.sec.gov/edgar/search/) directly."
+            return f"📄 **SEC Filings for {ticker}**\n\n❌ Error fetching SEC filings: {str(e)}"
 
+# =========================
+# News Utilities (Yahoo RSS)
+# =========================
 class NewsUtils:
     def __init__(self):
-        self.headers = {"User-Agent": "StockResearchMVP/1.0 (educational@example.com)"}
+        self.headers = {"User-Agent": "BullishMindsAI/1.0 (marcus@bullishmindsai.co.site)"}
+
     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.\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})"
+                return f"📰 **Latest News for {ticker}**\n\n❌ No recent news found via RSS feed."
             result = f"📰 **Latest News for {ticker}**\n\n"
             for i, entry in enumerate(feed.entries[:5]):
                 title = getattr(entry, 'title', 'No title')
@@ -134,28 +159,33 @@ 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)}\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})"
+            return f"📰 **Latest News for {ticker}**\n\n❌ Error fetching news: {str(e)}"
 
+# =========================
+# Chart Embed (TradingView)
+# =========================
 def get_tradingview_embed(ticker):
-    ticker = ticker.strip().upper() if ticker else "AAPL"
+    ticker = (ticker or "AAPL").strip().upper()
     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>'
 
+# =========================
+# Simulators/Calculators (Existing)
+# =========================
 def simulate_order_book(side, order_type, price, size, seed=123):
-    np.random.seed(seed)
+    np.random.seed(int(seed) if seed is not None else 123)
     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":
         if side == "Buy":
@@ -170,8 +200,8 @@ 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 sells_at_or_below.shape[0]:
-                    fill_price = sells_at_or_below.iloc[0]['Price']
+                if not sells_at_or_below.empty:
+                    fill_price = sells_at_or_below.iloc['Price']
                     fill_msg = f"Filled {size} @ {fill_price:.2f} (Aggressive Limit Buy)"
                 else:
                     queue_spot = 1 + np.random.randint(0, 3)
@@ -181,8 +211,8 @@ 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 buys_at_or_above.shape[0]:
-                    fill_price = buys_at_or_above.iloc[0]['Price']
+                if not buys_at_or_above.empty:
+                    fill_price = buys_at_or_above.iloc['Price']
                     fill_msg = f"Filled {size} @ {fill_price:.2f} (Aggressive Limit Sell)"
                 else:
                     queue_spot = 1 + np.random.randint(0, 3)
@@ -192,34 +222,90 @@ def simulate_order_book(side, order_type, price, size, seed=123):
     return df, fill_msg
 
 def slippage_estimator(side, order_size, seed=123):
-    np.random.seed(seed)
+    np.random.seed(int(seed) if seed is not None else 123)
     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 = np.random.randint(10, 70, len(levels))
-        prices = levels[levels > base_price]
-        sizes = sizes[levels > base_price]
+        sizes_all = np.random.randint(10, 70, len(levels))
+        mask = levels > base_price
+        prices = levels[mask]
+        sizes = sizes_all[mask]
     else:
-        sizes = np.random.randint(10, 70, len(levels))
-        prices = levels[levels < base_price]
-        sizes = sizes[levels < base_price]
-    remaining = order_size
+        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
+
     fills = []
     for p, s in zip(prices, sizes):
-        take = min(s, remaining)
-        fills.append((p, take))
-        remaining -= take
+        take = min(int(s), remaining)
+        if take > 0:
+            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
+    avg_fill = (df["Price"] * df["Shares"]).sum() / df["Shares"].sum()
     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
+# =========================
 sec_utils = SECUtils()
 news_utils = NewsUtils()
 
@@ -239,23 +325,24 @@ 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;}
 """
 
-with gr.Blocks(css=css, theme=gr.themes.Soft(), title="Bullish Minds AI - Stock Research & Education Platform") as demo:
-    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="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)
     gr.Markdown("""
 # **Bullish Minds AI**
-*Stock Research Platform MVP*
-
-**Comprehensive stock analysis, real-time data, and interactive education modules.**
-
-🎯 Enter a stock ticker symbol (**AAPL**, **TSLA**, **MSFT**, **GOOGL**) for market data, or check out the Lessons tab for learning modules!
+Stock Research Platform + Trading Curriculum
 
-⚠️ **Note**: Stock quote data from Polygon (Previous Close for free plans). Financial summary from Finnhub. Charts powered by TradingView.
+Honest, educational, and data‑rich. Use quotes, news, filings, and charts alongside interactive lessons. Not financial advice.
 """)
+
     with gr.Row():
         with gr.Column(scale=3):
             ticker_input = gr.Textbox(
@@ -264,8 +351,10 @@ with gr.Blocks(css=css, theme=gr.themes.Soft(), title="Bullish Minds AI - Stock
                 value="AAPL"
             )
         with gr.Column(scale=1):
-            refresh_btn = gr.Button("🔄 Refresh Data", variant="primary", size="lg")
+            refresh_btn = gr.Button("🔄 Refresh Data", variant="primary")
+
     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"):
@@ -278,64 +367,302 @@ with gr.Blocks(css=css, theme=gr.themes.Soft(), title="Bullish Minds AI - Stock
             gr.Markdown("### Interactive Price Chart")
             gr.Markdown("*Powered by TradingView*")
             chart_output = gr.HTML(get_tradingview_embed("AAPL"))
-        with gr.TabItem("🎓 Lessons"):
+
+        # ========== Education Tabs ==========
+        with gr.TabItem("🎓 Education"):
             with gr.Tabs():
-                # ================= LESSON 1 =================
-                with gr.TabItem("Lesson 1: Market Venues Explained"):
-                    gr.Markdown("## Lesson 1 — Market Venues: Exchanges, Dark Pools, Auction vs. Dealer Markets")
-                    # FULL Accordions and Markdown for Lesson 1 here (see earlier completions)
-                    # ... [lesson content omitted for brevity - paste  Lesson 1 text from previous completions here]
-                    gr.Markdown("---")
-                    gr.Markdown("*Try the tools below to visualize order book mechanics and slippage:*")
+                # Orientation
+                with gr.TabItem("0) Orientation"):
+                    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("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"
+                        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?"
                             )
-                # ================= LESSON 2 =================
-                with gr.TabItem("Lesson 2: Tickers, Floats, Market Cap, Sectors, Indices"):
-                    gr.Markdown("## Lesson 2 — Tickers, Floats, Market Cap, Sectors, Indices")
-                    # FULL Accordions and Markdown for Lesson 2 here (see earlier completions)
-                    # ... [lesson content omitted for brevity - paste Lesson 2 text from previous completions here]
-                # ================= LESSON 3 =================
-                with gr.TabItem("Lesson 3: Stocks, ETFs, ADRs, and Derivatives"):
-                    gr.Markdown("## Lesson 3 — Stocks vs. ETFs vs. ADRs; Basics of Options and Futures")
-                    # FULL Accordions and Markdown for Lesson 3 here (see earlier completions)
-                    # ... [lesson content omitted for brevity - paste Lesson 3 text from previous completions here]
+                            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)
+
+                            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)
+
+                        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.  
+""")
+                        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.  
+""")
+
+                # 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.  
+""")
 
     gr.Markdown("""
 ---
-**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.
+Data: Polygon (quotes), Finnhub (financials), Yahoo RSS (news), SEC EDGAR (filings), TradingView (charts).
+Troubleshooting: Check tickers, API keys, and retry if rate-limited.
 """)
 
     ticker_input.change(
@@ -346,7 +673,7 @@ with gr.Blocks(css=css, theme=gr.themes.Soft(), title="Bullish Minds AI - Stock
     refresh_btn.click(
         fn=update_stock_info,
         inputs=[ticker_input],
-        outputs=[quote_output, news_output, filings_output, financial_output, chart_output]
+        outputs=[quote_output, news_output, filings_output, chart_output]
     )
 
 if __name__ == "__main__":