Update app.py

app.py

@@ -1,11 +1,3 @@
-# app.py — Bullish Minds AI: Stock Research (original preserved) + Trading Curriculum (isolated)
-# Run: pip install gradio requests numpy pandas feedparser
-# Launch: python app.py
-# Notes:
-# - Polygon free plan: previous close endpoint used.
-# - Finnhub metrics require token.
-# - SEC fetch requires descriptive User-Agent.
-
 import gradio as gr
 import requests
 import numpy as np
@@ -16,14 +8,11 @@ import random
 from datetime import datetime
 import os
 
-# =========================================================
-# =============== ORIGINAL RESEARCH FEATURES ==============
-# =========================================================
-
-# API keys (ENV or original fallbacks)
+# === 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"
 
+# --- 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:
@@ -31,47 +20,45 @@ 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')
+            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()
-        metrics = (data or {}).get('metric', {}) or {}
+        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"
@@ -81,12 +68,12 @@ def get_financial_summary_finnhub(ticker, finnhub_api_key=FINNHUB_API_KEY):
     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)"}  # keep original UA if desired
-
+        self.headers = {"User-Agent": "StockResearchMVP/1.0 (educational@example.com)"}
     def get_cik(self, ticker):
         try:
             time.sleep(0.5)
@@ -94,14 +81,13 @@ class SECUtils:
             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)
@@ -132,6 +118,7 @@ class SECUtils:
         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."
 
+# --- News Utilities ---
 class NewsUtils:
     def __init__(self):
         self.headers = {"User-Agent": "StockResearchMVP/1.0 (educational@example.com)"}
@@ -154,11 +141,16 @@ 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 Chart Embed ---
 def get_tradingview_embed(ticker):
     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 TOOLS ---
 def simulate_order_book(side, order_type, price, size, seed=123):
     np.random.seed(seed)
     base_price = 100.00
@@ -169,7 +161,12 @@ def simulate_order_book(side, order_type, price, size, seed=123):
     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":
@@ -184,8 +181,8 @@ 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:
-                    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)
@@ -195,8 +192,8 @@ 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:
-                    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)
@@ -234,6 +231,7 @@ def slippage_estimator(side, order_size, seed=123):
     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()
 
@@ -253,80 +251,33 @@ def update_stock_info(ticker):
     chart_html = get_tradingview_embed(ticker)
     return quote_data, news_data, filings_data, financial_data, chart_html
 
-# =========================================================
-# =============== CURRICULUM (ISOLATED ADD) ===============
-# =========================================================
-
-# Education calculators (names isolated from original)
-def edu_rr_position_size(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 edu_atr_stop(entry: float, atr: float, atr_mult: float, direction: str) -> str:
-    if atr <= 0 or atr_mult <= 0:
-        return "ATR and multiplier must be > 0."
-    stop = entry - atr_mult * atr if direction == "Long" else entry + atr_mult * atr
-    return f"Suggested stop: {stop:.2f}"
-
-def edu_expectancy(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 edu_risk_of_ruin(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)"
-
-# =========================================================
-# ========================== 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") 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="Bullish Minds AI - Stock Research & Education Platform") as demo:
+    gr.Image("logo.png", elem_id="header-logo", show_label=False, show_download_button=False)
     gr.Markdown("""
 # **Bullish Minds AI**
-Stock Research Platform MVP + Trading Strategies Curriculum
+*Stock Research Platform MVP*
 
-Honest, educational, and data‑rich. Research with quotes, news, filings, charts; learn with interactive lessons. Not financial advice.
-""")
+**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!
+
+⚠️ **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(
                 label="Stock Ticker",
-                placeholder="Enter ticker (e.g., AAPL, TSLA, MSFT, GOOGL)",
+                placeholder="Enter ticker (e.g., AAPL, TSLA, MSFT)",
                 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 (unchanged wiring) --------
         with gr.TabItem("💰 Quote & Overview"):
             quote_output = gr.Markdown(value="Enter a ticker to see stock quote")
         with gr.TabItem("📰 News"):
@@ -339,341 +290,83 @@ Honest, educational, and data‑rich. Research with quotes, news, filings, chart
             gr.Markdown("### Interactive Price Chart")
             gr.Markdown("*Powered by TradingView*")
             chart_output = gr.HTML(get_tradingview_embed("AAPL"))
-
-        # -------- Education Tabs (isolated) --------
-        with gr.TabItem("🎓 Education"):
+        with gr.TabItem("🎓 Lessons"):
             with gr.Tabs():
-                # 0) Orientation
-                with gr.TabItem("0) Orientation"):
-                    gr.Markdown("""
-## Orientation & Disclaimers
-- Educational purpose only; not financial advice. Trading involves risk of loss, including principal.  
-- Suggested learning paths: Beginner → Foundations + Risk/Psych → Day or Swing → Validation → Compliance; Long‑Term track for investors.  
-- Time commitment: Foundations (3–5 hrs), Risk/Psych (2–3 hrs), Day or Swing Track (6–12 hrs), Validation/Compliance (3–5 hrs), Capstones (varies).  
+                with gr.TabItem("Lesson 1: Exchanges & Order Book"):
+                    gr.Markdown(
+"""### Lesson 1 — Market Venues: Exchanges, Dark Pools, Auction vs. Dealer Markets
+Level: Beginner • Estimated time: 25–35 minutes • Disclaimer: Educational only. Not financial advice.
+
+#### Why this matters
+Before you place any trade, it helps to know where your order goes and who it interacts with. Understanding market venues explains why a fill is fast or slow, why prices “jump,” and why a limit order protects you.
+
+#### Learning objectives
+By the end of this lesson, you will be able to:
+- Define exchanges, dark pools (ATS), auction markets, and dealer markets.
+- Explain how orders interact on a central limit order book versus with a dealer/market maker.
+- Describe what happens during opening/closing auctions and why they matter.
+- List pros/cons of lit exchanges and dark pools for typical retail traders.
+- Choose a suitable order type (market vs. limit) based on venue dynamics.
+
+#### Key terms (quick definitions)
+- **Exchange (lit venue):** A registered marketplace where bids/offers are displayed ("lit") and orders match by rules (e.g., price–time priority).
+- **Central Limit Order Book (CLOB):** The public queue of buy/sell limit orders at each price.
+- **NBBO:** National Best Bid and Offer; the best quoted prices across exchanges (US concept).
+- **Auction market:** Buyers and sellers compete by submitting orders; price emerges from order interaction (e.g., NYSE open/close auctions; continuous double auctions).
+- **Dealer market:** Dealers/market makers quote both bid and ask and trade against customers (e.g., wholesalers; historically Nasdaq).
+- **Market maker (MM):** A dealer obligated/encouraged to quote and provide liquidity.
+- **Dark pool / ATS:** Alternative Trading System where quotes are not displayed; orders often execute at or within NBBO.
+- **Price improvement:** Getting a better price than the displayed NBBO.
+- **Slippage:** Getting a worse execution price than expected due to fast moves or thin liquidity.
+
+#### [Full lesson continues...]
+[...scroll for more: Big picture, scenarios, review, glossary ...]
+
+""" + """
+---
+###### *Try the tools below to visualize order book mechanics and slippage:*
 """)
-                    gr.Markdown("### How to use this curriculum")
-                    gr.Markdown("- Read the short lessons, then use calculators/simulators. Complete quick quizzes and assignments. Keep a journal and review weekly.")
-
-                # 1) Market Foundations
-                with gr.TabItem("1) Foundations"):
                     with gr.Tabs():
-                        with gr.TabItem("1.1 Structure & Products"):
-                            gr.Markdown("""
-### Market Structure & Products
-Why it matters: Understanding where orders go and how prices form prevents unrealistic expectations about fills and slippage.  
-
-- Exchanges vs dark pools: Lit venues form NBBO; dark pools internalize flow off‑exchange. Auction (price discovery via auction book) vs dealer (market makers quote two‑sided markets).  
-- Tickers, float, market cap, sectors, indices: Float constrains supply; sector/Index membership affects passive flows and correlations.  
-- Products overview: Stocks (ownership), ETFs (baskets; creation/redemption), ADRs (foreign shares), options/futures (context only here).  
-""")
-                            gr.Markdown("#### Interactive: Microstructure tools")
-                            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(label="Order Book"), 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(label="Fill Breakdown")],
-                                        live=False,
-                                        allow_flagging="never"
-                                    )
-                            gr.Markdown("#### Quiz")
-                            f_q1 = gr.Radio(
-                                ["Exchanges and dark pools route orders differently", "Dark pools set the NBBO", "Dealer markets have no market makers"],
-                                label="Which is true?"
+                        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"
                             )
-                            f_q1_out = gr.Markdown()
-                            def f_q1_check(ans):
-                                return "Correct." if ans == "Exchanges and dark pools route orders differently" else "Review: NBBO is lit; dealers do make markets."
-                            f_q1_btn = gr.Button("Submit")
-                            f_q1_btn.click(f_q1_check, f_q1, f_q1_out)
-
-                            gr.Markdown("#### Assignment")
-                            gr.Markdown("- Watch the tape (L2/T&S) for a liquid ticker at open for 5 minutes; note spread, prints, and any imbalance behavior. Summarize how it would affect a market vs limit order.")
-
-                        with gr.TabItem("1.2 Accounts & Orders"):
-                            gr.Markdown("""
-### Accounts, Brokers & Order Execution
-Why it matters: Order choice and account rules largely determine slippage, fills, and survivability.  
-
-- Cash vs margin accounts; PDT basics (US) and leverage risks; borrow fees for shorts.  
-- Order types: market, limit, stop, stop‑limit, trailing; OCO and bracket orders to pre‑plan exits.  
-- Slippage, spreads, liquidity; when L2/T&S help vs harm (noise).  
-""")
-                            gr.Markdown("#### Assignment")
-                            gr.Markdown("- For a ticker of choice, write when to use a market order vs a limit order in three scenarios: breakout, pullback, and thin after‑hours.")
-
-                        with gr.TabItem("1.3 Fees & Taxes"):
-                            gr.Markdown("""
-### Fees, Taxes & Recordkeeping
-Why it matters: Friction and taxes compound; knowing costs improves net expectancy.  
-
-- Commissions vs PFOF; short borrow fees; margin interest and compounding risk.  
-- Taxes (high level): short vs long‑term gains; wash sale basics; consult a tax professional for personal advice.  
-- Trade journal: screenshots, tags, metrics (MAE/MFE, adherence) to drive improvement.  
-""")
-                            gr.Markdown("#### Assignment")
-                            gr.Markdown("- Create a simple trade log template with fields: ticker, setup tag, hypothesis, entry/stop/targets, size, MAE/MFE, slippage, adherence, emotions, lesson learned.")
-
-                        with gr.TabItem("1.4 Charts & Data"):
-                            gr.Markdown("""
-### Charts, Timeframes & Data
-Why it matters: Multi‑timeframe context filters noise; data events explain volatility.  
-
-- Candles, OHLC, volume; top‑down multi‑timeframe analysis (weekly→daily→intra).  
-- Indicators (context): MA (trend), RSI (momentum), MACD (trend/momentum), ATR (volatility), VWAP (volume‑weighted mean).  
-- Data drivers: economic calendar, earnings, splits, dividends and why surprises move price.  
-""")
-                            gr.Markdown("#### Assignment")
-                            gr.Markdown("- Pick a stock. Mark weekly trend, daily key levels, and one intraday trigger you would consider. Explain why.")
-
-                # 2) Risk & Psychology
-                with gr.TabItem("2) Risk & Psychology"):
-                    with gr.Tabs():
-                        with gr.TabItem("2.1 Risk Core"):
-                            gr.Markdown("""
-### Risk Management Core
-Why it matters: Survival precedes success; math beats hope.  
-
-- Risk‑per‑trade sizing from stop distance (ATR/structure).  
-- Reward:risk, win rate, expectancy; drawdown controls; circuit breakers and daily max loss.  
-""")
-                            with gr.Row():
-                                with gr.Column():
-                                    acct = gr.Number(label="Account Equity ($)", value=5000)
-                                    riskpct = gr.Slider(0.1, 5, value=1.0, step=0.1, label="Risk per Trade (%)")
-                                    entry = gr.Number(label="Entry Price", value=100.0)
-                                    stop = gr.Number(label="Stop Price", value=98.0)
-                                    calc_btn = gr.Button("Position Size & Targets")
-                                with gr.Column():
-                                    rr_out = gr.Textbox(label="Sizing/Targets", lines=6)
-                            calc_btn.click(edu_rr_position_size, [acct, riskpct, entry, stop], rr_out)
-
-                            gr.Markdown("#### Expectancy")
-                            wr = gr.Slider(10, 90, value=45, step=1, label="Win Rate (%)")
-                            avg_win = gr.Number(label="Avg Win ($)", 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(edu_expectancy, [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_slider = 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(edu_risk_of_ruin, [wr2, rr_slider, bankrisk], ror_out)
-
-                            gr.Markdown("#### Assignment")
-                            gr.Markdown("- Define personal circuit breakers: max daily loss, max trades, and stop‑trading conditions after consecutive losers. Write them into a visible checklist.")
-
-                        with gr.TabItem("2.2 Psychology"):
-                            gr.Markdown("""
-### Trader Psychology
-Why it matters: Edge dies without discipline; emotions tax returns.  
-
-- Biases: loss aversion, FOMO, recency; use if‑then rules to pre‑commit behavior.  
-- Routines: pre/post‑market checklists; weekly reviews; accountability partners.  
-- Overtrading controls: enforced timeouts, trade count limits; set alarms.  
-""")
-                            gr.Markdown("#### Assignment")
-                            gr.Markdown("- Draft a one‑page ruleset. Include: allowed setups, entry/stop/target rules, risk per trade, when to stop trading, and review cadence.")
-
-                # 3) 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 constraints; ideal markets (liquid large caps, high RVOL).  
-""")
-                        with gr.TabItem("3.2 Intraday Setups"):
-                            gr.Markdown("""
-### Core Intraday Setups
-- ORB & Pullback: Define the first 5–15m range; trade break with tight invalidation; look for pullback to ORB top with volume confirmation.  
-- VWAP Trend vs Reversion: Trend days ride VWAP as dynamic support/resistance; on mean‑reversion days, fades back to VWAP with strict risk.  
-- Momentum Ignition: Identify high RVOL + catalyst; enter on reclaim of key level with tape confirming; tighten quickly.  
-- Mean‑Reversion to PDH/PDL and Gap‑Fills: Use prior day levels as magnets; require exhaustion/taper on approach; avoid fighting strong trends.  
-- News/Catalyst + Halts: Understand halt codes; avoid chasing first prints; trade post‑halt structure if liquidity permits.  
-""")
-                            gr.Markdown("#### Practice")
-                            gr.Markdown("- Pick two days for a liquid ticker. Mark ORB, VWAP, PDH/PDL. Describe one A+ and one avoid trade with exact trigger/stop/targets.")
-                        with gr.TabItem("3.3 Tools & Levels"):
-                            gr.Markdown("""
-### Tools & Levels
-- Pre‑market prep: gap scan (percent + RVOL), news filter, float awareness.  
-- Key levels: pre‑market H/L, PDH/PDL, weekly pivots, pre‑identified inflection areas.  
-- Tape reading: value for liquidity/urgency; ignore when it induces noise.  
-""")
-                        with gr.TabItem("3.4 Execution & Risk"):
-                            gr.Markdown("""
-### Execution & Risk
-- Scaling in/out, partials; dynamic stops (structure/ATR).  
-- Hotkeys and bracket orders to reduce latency; enforce max daily loss and trade count.  
-""")
-                        with gr.TabItem("3.5 Playbooks & Cases"):
-                            gr.Markdown("""
-### Playbooks & Case Studies
-- Template: hypothesis, trigger, invalidation, targets, management.  
-- Build A+ examples with screenshots and metrics (MAE/MFE, RR, adherence).  
-""")
-                        with gr.TabItem("3.6 Metrics & Review"):
-                            gr.Markdown("""
-### Metrics & Review
-- KPIs: expectancy, MAE/MFE, avg hold, adherence. Weekly scorecard and top‑3 fixes to guide next week.  
-""")
-
-                # 4) 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; time efficiency; accepting overnight gap risk; higher signal‑to‑noise on daily/weekly frames.  
-""")
-                        with gr.TabItem("4.2 Core Setups"):
-                            gr.Markdown("""
-### Core Swing Setups
-- Breakout from Base / Volatility Contraction: Tight pattern + rising RS; buy pivot with volume burst; cut below structure.  
-- Pullback to 20/50‑MA: Trend intact; pullback on light volume; reversal bar + volume confirmation; stop under swing low.  
-- Break of Structure / Higher‑Low Retest: Trend change; enter on HL confirming with volume; stop under HL.  
-- Range Trading with ATR Stops: Define boundaries; enter near support with confluence; ATR‑based stop; partials mid‑range.  
-- Earnings Season Plays: Pre‑/post‑earnings patterns; beware gaps; reduce size ahead of binary events unless strategy-specific edge.  
-""")
-                        with gr.TabItem("4.3 Scanning & Watchlists"):
-                            gr.Markdown("""
-### Scanning & Watchlists
-- Relative strength vs sector/index; liquidity screens; ATR filters; basic fundamental overlays (EPS growth, margins, debt load, sales acceleration).  
-""")
-                        with gr.TabItem("4.4 Entries/Stops/Targets"):
-                            gr.Markdown("""
-### Entries, Stops, and Profit Taking
-- Stops: structural or ATR‑based; move to breakeven only when plan says; avoid premature tightening.  
-- Pyramid/scale techniques; partial profit frameworks (e.g., take 1/3 at 2R, trail remainder).  
-- Managing gaps and news risk proactively.  
-""")
-                            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(edu_atr_stop, [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 concurrent names; volatility budgeting; optional options for risk shaping (protective puts/collars).  
-""")
-                        with gr.TabItem("4.6 Review Cycle"):
-                            gr.Markdown("""
-### Review Cycle
-- Weekly prep (Sunday routine), mid‑week check‑ins; constant playbook refinement and journaling.  
-""")
-
-                # 5) Long-Term Investing
-                with gr.TabItem("5) Long‑Term"):
-                    with gr.Tabs():
-                        with gr.TabItem("5.1 Foundations"):
-                            gr.Markdown("""
-### Investing Foundations
-- Time horizon, risk capacity vs tolerance; DCA vs lump sum; sequence‑of‑returns risk and staying the course.  
-""")
-                        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 (calendar vs threshold).  
-""")
-                        with gr.TabItem("5.3 Equity Selection (Optional)"):
-                            gr.Markdown("""
-### Equity Selection (Optional Stock‑Picking)
-- Business quality: moats, ROIC, FCF, balance sheet; quick valuation snapshots (PE, EV/EBITDA, DCF intuition); dividend growth strategies and payout ratios.  
-""")
-                        with gr.TabItem("5.4 Behavior & Discipline"):
-                            gr.Markdown("""
-### Behavior & Discipline
-- Avoid panic buy/sell; 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; harvesting concepts. Consult a tax professional for personal advice.  
-""")
-
-                # 6) Strategy 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 and look‑ahead bias; walk‑forward validation; out‑of‑sample testing; paper before real.  
-""")
-                        with gr.TabItem("6.2 Paper → Capital"):
-                            gr.Markdown("""
-### Paper Trading & Phased Capital Deployment
-- Sim → micro size → scale on adherence and expectancy KPIs; cut size after rule breaches.  
-""")
-                        with gr.TabItem("6.3 KPIs & Edge"):
-                            gr.Markdown("""
-### KPIs & Edge Tracking
-- Define edge precisely; maintain setup playbooks; build an expectancy/KPI dashboard and review weekly.  
-""")
-
-                # 7) Compliance, Ethics & Safety
-                with gr.TabItem("7) Compliance"):
-                    gr.Markdown("""
-### Compliance, Ethics & Safety
-- PDT (US) overview; margin basics; short‑selling mechanics, locate/borrow fees; Reg SHO context.  
-- Earnings and MNPI; avoid rumor/pump behavior; platform safeguards: max daily loss, time‑outs, risk limits.  
-""")
-
-                # 8) Capstones & Certifications
-                with gr.TabItem("8) Capstones"):
-                    gr.Markdown("""
-### Capstones & Certifications
-- Day Trading: 20 simulated trades across 3 setups with predefined risk and full journals; weekly review presentation.  
-- Swing Trading: Manage a 5‑name swing portfolio for 6 simulated weeks; submit hypothesis plans and results.  
-- Long‑Term: Write an IPS and backtest a DCA plan through historical drawdowns; present stress‑test responses.  
-""")
-
-                # 9) App‑Native Components
-                with gr.TabItem("9) App‑Native"):
-                    gr.Markdown("""
-### App‑Native Components (Build Plan)
-- 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: trade log with tags, screenshots, reasons to enter/exit, emotions.  
-""")
+                        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 here as needed
 
     gr.Markdown("""
 ---
-Data sources: 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],