Deep dive into analysis

📊 Stock Analysis Workflow

System: ML Analysis Pipeline · AI Valuation Engine · Schwab GEX Module · Agentic AI

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Table of Contents

1. System Architecture Overview
2. Step 1 — Ticker Selection
3. Step 2 — Fundamental Analysis
4. Step 3 — DCF Valuation (AI)
5. Step 4 — Machine Learning Forecast
6. Step 5 — Backtesting (MA Strategy)
7. Step 6 — Sentiment Analysis
8. Step 7 — Quantitative Risk Analysis
9. Step 8 — Macroeconomic Analysis
10. Step 9 — GEX (Gamma Exposure)
11. Step 10 — Weighted Score & Signal
12. Step 11 — AI Final Summary
13. Step 12 — Agentic AI (Alternative Pipeline)
14. Output — Email Report
15. Configuration Reference

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1. System Architecture Overview

┌─────────────────────────────────────────────────────────────────────┐
│                     fetch_ML_v2.py / ML_report_v2.py                │
│                                                                       │
│  [Ticker Selection]                                                   │
│       │                                                               │
│       ▼                                                               │
│  ┌─────────────┐  ┌───────────┐  ┌──────────┐  ┌────────────────┐   │
│  │ Fundamental │  │    DCF    │  │    ML    │  │  Backtesting   │   │
│  │  Analysis   │  │  (AI LLM) │  │ Forecast │  │  (MA Strategy) │   │
│  └──────┬──────┘  └─────┬─────┘  └────┬─────┘  └───────┬────────┘   │
│         │               │             │                 │             │
│  ┌──────▼──────┐  ┌──────▼──────┐  ┌──▼──────────┐  ┌──▼──────────┐ │
│  │  Sentiment  │  │    Quant    │  │    Macro    │  │    GEX      │ │
│  │  (FinBERT)  │  │ (QuantStats)│  │   (FRED)   │  │  (Schwab)   │ │
│  └──────┬──────┘  └─────┬───────┘  └────┬────────┘  └─────┬───────┘ │
│         │               │               │                  │          │
│         └───────────────┴───────────────┴──────────────────┘          │
│                                    │                                   │
│                          [Weighted Score]                             │
│                                    │                                   │
│                          [AI Summary Report]                          │
│                                    │                                   │
│                    [Email + Database Insert]                          │
└─────────────────────────────────────────────────────────────────────┘

---

2. Step 1 — Ticker Selection

Source: Stock Screener Utility — Sector & momentum-ranked stock picker

How It Works

The screener finds the single highest-momentum, highest-liquidity stock each day using Finviz.

1. Sector finder
   └─ Overview → all sectors sorted by daily % change
   └─ Pick the TOP sector (best-performing sector today)

2. Ticker finder
   └─ Custom screener with filters:
       • Index:        Any
       • Country:      USA
       • Performance:  Today Up
       • Gap:          Up  (opening gap up)
   └─ Order by: % Change (descending)
   └─ Take top 25 results
   └─ Calculate Trade Value = Price × Volume
   └─ Sort by Trade Value (descending)
   └─ Return #1 ticker  ← highest momentum + highest liquidity

Screener Columns Pulled

Column	Description
Ticker	Symbol
Price	Current price
Change	Daily % change
Volume	Share volume
P/E	Price/Earnings
EPS this Y	EPS growth this year
EPS next Y	EPS growth next year
Sales Q/Q	Revenue growth QoQ
P/FCF	Price/Free Cash Flow
P/B	Price/Book
ROI	Return on Investment
ROE	Return on Equity

3. Step 2 — Fundamental Analysis

Source: Fundamental Data Fetcher — Multi-source stock fundamentals aggregator (Finviz + yfinance)

Data Sources

Source	Data
Finviz (finvizfinance)	P/E, P/B, P/S, ROA, ROE, gross margin, operating margin, EPS growth, short float, insider own%
yfinance	Company info, valuation, earnings, balance sheet, analyst targets, insider transactions, earnings dates

Key Metrics Collected

Valuation

• Market Cap, Enterprise Value
• Trailing P/E, Forward P/E, PEG Ratio
• Price/Book, Price/Sales, EV/EBITDA, EV/Revenue

Profitability

• EPS (trailing & forward)
• Revenue & Revenue Growth
• Gross Margins, Operating Margins, Profit Margins
• ROE (Return on Equity), ROA (Return on Assets)

Balance Sheet Health

• Total Cash, Total Debt, Debt/Equity Ratio
• Current Ratio
• Free Cash Flow, Operating Cash Flow

Price & Targets

• Current Price
• Analyst Target (Low / Mean / Median / High)
• 52-Week High/Low
• 50-Day / 200-Day Moving Averages
• Dividend Yield, Payout Ratio

Qualitative

• Recent analyst recommendations (last 5)
• Insider transactions (last 5)
• Upcoming earnings dates

---

4. Step 3 — DCF Valuation (AI)

Source: AI-Powered DCF Valuation Engine — 7-step discounted cash flow analysis

Inputs (from yfinance)

ticker_yf.cashflow        # Cash flow statement
ticker_yf.balance_sheet   # Balance sheet
ticker_yf.income_stmt     # Income statement (P&L)
company_info              # Key stats dict

AI DCF Methodology

The AI model is instructed to follow this exact 7-step process:

Step 1 ─ Forecast 5 years of Free Cash Flow (FCF)
          • Based on historical FCF trend
          • Max 20% annual growth unless justified
          • No negative forecasted FCF

Step 2 ─ Terminal Value (Gordon Growth Model)
          TV = Final Year FCF × (1 + g) / (WACC - g)

Step 3 ─ WACC Calculation
          • Cost of Equity = CAPM: rf + β × (rm - rf)
          • Cost of Debt = avg interest rate × (1 - tax rate)
          • Capital structure = Debt / (Debt + Equity)
          • WACC = wE × rE + wD × rD

Step 4 ─ Discount all FCFs + Terminal Value to PV
          PV = FCF_t / (1 + WACC)^t

Step 5 ─ Subtract Net Debt
          Equity Value = Σ PV(FCF) + PV(TV) - Net Debt
          Net Debt = Total Debt - Cash

Step 6 ─ Intrinsic Share Price
          Price = Equity Value / Shares Outstanding

Step 7 ─ Output: Intrinsic value (USD total) + per-share price

Output

Intrinsic Share Price: $XXX.XX Comparison to current market price → overvalued / fair / undervalued

---

5. Step 4 — Machine Learning Forecast

Source: ML Forecasting Engine — Bidirectional LSTM + XGBoost ensemble price predictor

Data Preparation

• Price history: 3 years from yfinance
• Lookback window: 60 trading days
• Forecast horizon: 20 trading days
• Train/Test split: 80% / 20%

Feature Engineering

# Price features
returns          = Close.pct_change()
log_returns      = log(Close / Close.shift(1))

# Moving averages (ratios — normalized)
sma_5/10/20/50   = Close.rolling(window).mean()
sma_ratio        = Close / sma

# Volatility
volatility_20    = returns.rolling(20).std()

# RSI (14-period)
rsi = 100 - (100 / (1 + avg_gain / avg_loss))

# MACD
macd             = EMA(12) - EMA(26)
macd_signal      = EMA(macd, 9)

# Bollinger Bands
bb_mid           = SMA(20)
bb_upper         = SMA(20) + 2 × std(20)
bb_lower         = SMA(20) - 2 × std(20)
bb_position      = (Close - bb_lower) / (bb_upper - bb_lower)

Model Architecture

LSTM (Primary)

Input: (60 days × 1 feature — scaled Close price)
  ↓
Bidirectional LSTM (128 units) → BatchNorm → Dropout(0.3)
  ↓
LSTM (64 units) → BatchNorm → Dropout(0.3)
  ↓
LSTM (32 units) → BatchNorm → Dropout(0.2)
  ↓
Dense(16, ReLU) → Dense(1)

Optimizer:  Adam (lr=0.001)
Loss:       Huber (robust to outliers)
Callbacks:  EarlyStopping (patience=5), restore_best_weights=True
Epochs:     10 max

XGBoost (Ensemble Pair)

XGBRegressor(
    n_estimators=100,
    max_depth=6,
    learning_rate=0.1,
    subsample=0.8,
    colsample_bytree=0.8
)

Ensemble

Final Prediction = (LSTM prediction + XGBoost prediction) / 2

Forecast Output

RMSE              : Root Mean Squared Error
MAE               : Mean Absolute Error
MAPE              : Mean Absolute Percentage Error
Model Accuracy    : max(0, 100 - MAPE) %
Forecast Trend    : "Up" or "Down"
Expected Return   : (last_forecast - first_forecast) / first_forecast
forecast_df       : DataFrame of 20-day price forecast

---

6. Step 5 — Backtesting (MA Strategy)

Source: MA Strategy Backtester — Vectorized moving average signal simulator

Strategy Logic

For each MA window in [5, 7, 9, 12, 15, 20, 50]:
  ┌──────────────────────────────────────┐
  │ Entry Signal                         │
  │   |Price - MA| / MA < 1%            │
  │   (price touching the moving average)│
  └──────────────────────────────────────┘
            │
            ▼
  ┌──────────────────────────────────────┐
  │ Exit Signal                          │
  │   Take Profit:  PnL ≥ +5%           │
  │   Stop Loss:    PnL ≤ -5%           │
  └──────────────────────────────────────┘

• Data: yfinance from 2022-01-01 to yesterday
• Starting capital: $10,000
• Benchmark: Buy-and-Hold return over same period

Output Per MA Window

Metric	Description
total_trades	Number of trades executed
win_rate	% of profitable trades
return_pct	Total strategy return %
final_capital	End capital from $10,000
avg_holding_days	Average days held per trade

Selection Criteria

Best MA Window = highest return_pct across all windows Feeds into: best_sma stored in database, best_holding days

---

7. Step 6 — Sentiment Analysis

Source: Sentiment Analysis Pipeline — FinBERT NLP engine for news & Reddit crowd signals

Sentiment Models

Model	Role
FinBERT (ProsusAI/finbert)	Primary — fine-tuned on financial text
VADER + custom finance lexicon	Fallback if FinBERT unavailable

Custom Finance Lexicon (VADER additions)

'bullish': +3.7   'bearish': -3.7
'buy': +4.0       'sell': -4.0
'upgrade': +3.0   'downgrade': -3.0
'beat': +2.5      'miss': -2.5
'moon': +4.0      'crash': -3.5
'rally': +2.5     'squeeze': +2.0

News Analysis

Sources:

• Finviz — recent news headlines for the ticker
• Yahoo Finance — yf.Ticker(ticker).news

Process:

Fetch headlines → Deduplicate → FinBERT scoring →
Average compound score → Return {score, pos%, neg%, neu%, headline_count}

Reddit Analysis

Subreddits: wallstreetbets, stocks, investing, stockmarket

Filters:

• Upvote ratio ≥ 0.70
• Upvotes ≥ 20
• Checks post title + body + top comments

Process:

Parallel fetch (4 workers) across subreddits →
Match ticker symbol or company name →
FinBERT scoring on matching posts/comments →
Average compound score → Return {score, mention_count}

---

8. Step 7 — Quantitative Risk Analysis

Source: Quantitative Risk Analyzer — QuantStats performance metrics + Risk/Return vs S&P 500 charting

QuantStats Metrics (from 2022-01-01)

Metric	Meaning
Sharpe Ratio	Risk-adjusted return (rf = 4% annual)
Sortino Ratio	Downside-adjusted return
Calmar Ratio	CAGR / Max Drawdown
Max Drawdown	Worst peak-to-trough loss
Volatility	Annualized standard deviation of returns
Win Rate	% of positive daily returns
Profit Factor	Gross profit / Gross loss
CAGR	Compound Annual Growth Rate
Value at Risk (VaR)	95% worst-case daily loss
Expected Return	Mean daily return

Risk vs Return Chart

MarketVsticker.generate_vsticker() produces a scatter plot:

• X-axis: Risk (daily return std dev)
• Y-axis: Average return × 10
• Plots ticker vs S&P 500
• Red dashed line = S&P 500 slope (Sharpe reference line)

Tickers above the S&P line offer better risk-adjusted returns

---

9. Step 8 — Macroeconomic Analysis

Source: Macroeconomic Analysis Engine — FRED API data tracker with VIX and BofA Bear/Bull indicator scoring

Data Sources

Source	Series	Description
FRED API	GDP	Gross Domestic Product
	FEDFUNDS	Federal Funds Rate
	CPIAUCSL	CPI — inflation
	PPIACO	Producer Price Index
	DGS10	10-Year Treasury Yield
	DGS2	2-Year Treasury Yield
	UNRATE	Unemployment Rate
	USSLIND	Leading Economic Index
	HOUST	Housing Starts
	PERMIT	Building Permits
	UMCSENT	Consumer Confidence
Yahoo Finance	^VIX	Volatility Index
	SPY, QQQ, IWM	Market breadth

VIX Classification

VIX Level	Classification
< 15	Low (complacent)
15–20	Normal
20–25	Elevated
25–30	High
> 30	Extreme (fear)

Yield Curve Analysis

Spread = 10Y Treasury Yield - 2Y Treasury Yield

Spread < 0  →  ⚠️ INVERTED (Recession warning)
Spread > 0  →  ✅ Normal

Market Breadth (SPY / QQQ / IWM)

For each index: 1D, 1W, 1M, 3M, YTD returns + trend classification:

• strong uptrend / uptrend / sideways / downtrend / strong downtrend

BofA Bear Indicators (10 signals — Market Peak Warnings)

#	Signal	Threshold
1	Consumer Confidence	> 110 (prior 60mo)
2	Net % expecting stocks higher	> 20% (prior 6mo)
3	Sell-side indicator	“Sell” signal
4	S&P 500 LT growth 5yr Z-score	> 1
5	M&A deals 10yr Z-score	> 1
6	(Trailing PE + YoY CPI) Z-score	> 1
7	Low PE underperforms High PE	by 2.5ppt (6mo)
8	Inverted yield curve	Spread < 0
9	Credit stress indicator	< 0.25
10	Tightening credit (SLOOS)	Triggered

BofA Bull Indicators (10 signals — Market Bottom Signals)

#	Signal	Threshold
1	Fed cutting rates	Prior 12mo
2	Unemployment rising	vs 12mo low
3	More bears than bulls	AAII survey
4	ERP increase	> 75bps vs 12mo low
5	2Y yield decline	> 50bps vs 6mo high
6	Sell-side indicator	“Buy” signal
7	Yield curve steepening	vs 6mo low
8	5% bear market rally	Prior 3mo
9	Rule of 20 triggered	—
10	PMI improves	vs 12mo YoY low

Macro Sentiment Classification

Bear signals - Bull signals > 2  →  BEARISH
Bull signals - Bear signals > 2  →  BULLISH
Otherwise                        →  NEUTRAL

Macro Score Adjustment

if macro_sentiment == 'bearish' or bear_count >= 5:
    macro_adjustment = -0.05   # Reduce buy conviction
elif macro_sentiment == 'bullish' or bull_count >= 5:
    macro_adjustment = +0.02   # Slightly increase conviction

if VIX > 30:
    macro_adjustment -= 0.03   # Additional caution in fear spike

---

10. Step 9 — GEX (Gamma Exposure)

Source: Schwab GEX Calculator — Dealer gamma exposure derived from live Schwab option chain feed
API: Charles Schwab Live Market Data — Real-time option chain endpoint (/marketdata/v1/chains)

What Is GEX?

GEX measures how much gamma market makers (dealers) hold, which predicts near-term market behavior:

GEX Formula:
  Call GEX =  Gamma × Open Interest × 100 × Spot Price  (positive)
  Put  GEX = -Gamma × Open Interest × 100 × Spot Price  (negative)

Total GEX = Σ(Call GEX) + Σ(Put GEX) across all strikes

Market Condition Interpretation

Condition	Meaning	Market Behavior
Positive GEX (Total > 0)	Dealers are long gamma	Mean-reverting, lower volatility, price pinned to large strikes
Negative GEX (Total < 0)	Dealers are short gamma	Trending/accelerating, higher volatility, moves amplified

Key GEX Levels

GEX Flip Level  — Strike where cumulative GEX crosses zero
                  Acts as a market pivot / support-resistance

Support Level   — Highest |GEX| strike below current price
                  Large positive GEX below = dealers hedge by buying → support

Resistance Level — Highest |GEX| strike above current price
                   Large positive GEX above = dealers hedge by selling → resistance

0DTE GEX

Special calculation for same-day expiration options only (highest gamma impact near expiry). Useful for intraday trading levels.

GEX Report Output

═══════════════════════════════════
         GEX ANALYSIS: SPY
═══════════════════════════════════
📊 Current Price: $565.43
💰 Total GEX: +$2.14B

📈 Market Condition: POSITIVE_GAMMA
   Dealers long gamma → Market likely to mean-revert

🎯 Key Levels:
   • GEX Flip:    $558.00
   • Support:     $560.00
   • Resistance:  $570.00

📍 Top Positive GEX Strikes (Resistance):
   • $570.00: $420.5M
   • $575.00: $380.2M
   ...

Covered Call Screener (bonus)

Source: Covered Call Screener — High-IV option income opportunity finder using Schwab option chains

Screens for high-IV covered call opportunities:

Metric	Formula
Premium Yield	bid / spot price
Annualized Return	premium_yield × 365/DTE
Downside Protection	bid / spot price
Theta/Gamma Ratio	θ / Γ (higher = better risk/reward)

Filters: DTE 14–60, OTM 1%–15%, min OI 10, configurable IV floor

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11. Step 10 — Weighted Score & Signal

Source: ML Analysis Orchestrator — Weighted signal aggregator that combines all module outputs with macro adjustment

Score Weights

                    ┌─────────────────────────────────────────────────┐
                    │           WEIGHTED SCORE CALCULATION            │
                    │                                                  │
   News Sentiment   │  score × 0.30  (FinBERT compound, range -1 to 1)│
   Reddit Sentiment │  score × 0.20  (FinBERT compound, range -1 to 1)│
   ML Forecast      │  return × 0.30 (expected 20-day return decimal) │
   Buy-and-Hold     │  return × 0.20 (historical BnH return decimal)  │
                    │  ─────────────────────────────────────────────── │
                    │  raw_score  = sum of above                       │
                    │  + macro_adjustment  (-0.08 to +0.02)            │
                    │  ─────────────────────────────────────────────── │
                    │  weighted_score = raw_score + macro_adjustment   │
                    └─────────────────────────────────────────────────┘

Signal Decision

if weighted_score > 0.5:
    signal = "Buy"
else:
    signal = "Hold"

Database Record (tk_selected table)

INSERT INTO tk_selected (date, ticker, score, news, reddit, lstm, holding_days, sma, result)
VALUES (today, ticker, weighted_score, news_score, reddit_score, forecast_trend, best_holding, best_ma, signal)

---

12. Step 11 — AI Final Summary

Source: AI Investment Report Generator — Multi-factor synthesis engine that produces a buy-side equity memo

Inputs Fed to AI Model

═══ FUNDAMENTALS ═══        (Finviz + yfinance table)
═══ TECHNICAL CHART ═══     (Matplotlib chart: price + MA + forecast + sentiment + backtest)
═══ ML ANALYSIS REPORT ═══  (Full text report from generate_report())
═══ QUANTITATIVE METRICS ══ (QuantStats summary)
═══ MACROECONOMIC ANALYSIS ═(FRED + VIX + BofA signals)
═══ GEX ANALYSIS ═══        (Schwab GEX report)
═══ DCF VALUATION ═══       (AI DCF output)

Output Structure

1. Executive Summary (1-2 paragraphs)
   └─ Valuation, market position, technicals, macro, sentiment

2. Macroeconomic Context
   └─ VIX level, yield curve status, market breadth (SPY/QQQ/IWM)
   └─ BofA Bear/Bull signals
   └─ Risk-on / Risk-off assessment

3. GEX Analysis
   └─ Positive vs Negative gamma interpretation
   └─ Key GEX support/resistance/flip levels
   └─ Position sizing guidance from GEX

4. Fundamental Strengths & Risks (bullet points)

5. Sentiment Assessment
   └─ News vs Reddit divergence analysis

6. Risk/Return Positioning
   └─ Volatility, drawdowns, win rate

7. Model Alignment
   └─ MA backtest vs LSTM forecast alignment

8. DCF Comparison
   └─ Intrinsic value vs current price

9. ─────────────────────────────────────────────────────
   🟡 Final Recommendation (as of YYYY-MM-DD)
   ▶ Position: BUY / HOLD / SELL
   ▶ Macro Alignment: FAVORABLE / NEUTRAL / UNFAVORABLE
   ▶ GEX Condition: POSITIVE GAMMA / NEGATIVE GAMMA

   Key Notes:
   - Macro Context: [VIX, yield curve, market trend]
   - GEX Levels: [Support $X, Resistance $Y, Flip $Z]
   - Tactical entry/exit ranges
   - Position sizing guidance
   - Position management (trailing stop, profit target)
   - 1-line Summary Statement
   ─────────────────────────────────────────────────────

---

13. Step 12 — Agentic AI (Alternative Pipeline)

Source: Agentic AI Orchestrator & Specialist Agents — LLM-first sequential analysis pipeline with four domain-expert personas

This is an LLM-first alternative to the quantitative pipeline, useful for deeper qualitative analysis.

Agent Execution Flow

User provides ticker(s)
        │
        ▼
┌─────────────────────────────────────────────────────────────────┐
│  Step 1: Macro Analyst (runs ONCE, shared across all tickers)   │
│          Skill: macroeconomic (FRED + VIX + market data)         │
│          Output: RISK-ON / RISK-OFF / TRANSITIONAL              │
└───────────────────────────────┬─────────────────────────────────┘
                                │
                                ▼
┌─────────────────────────────────────────────────────────────────┐
│  Step 2: Fundamental Analyst (per ticker)                        │
│          Skills: microeconomic + qualitative                     │
│          Data: yfinance financials, DCF, sector comps, news      │
│          Output: OVERVALUED / FAIR / UNDERVALUED + catalysts     │
└───────────────────────────────┬─────────────────────────────────┘
                                │
                                ▼
┌─────────────────────────────────────────────────────────────────┐
│  Step 3: Quant Analyst (per ticker)                              │
│          Skill: quantitative                                     │
│          Data: Price history, MA structure, RSI/MACD, Sharpe,   │
│                max drawdown, beta, LSTM forecasts, GEX           │
│          Output: Technical signal + risk metrics + entry/exit    │
└───────────────────────────────┬─────────────────────────────────┘
                                │
                                ▼
┌─────────────────────────────────────────────────────────────────┐
│  Step 4: Portfolio Strategist (synthesis)                        │
│          Receives: all 3 agent analyses                          │
│          Output: BUY/HOLD/SELL + conviction (1-10) +            │
│                  position size + risk parameters                 │
└─────────────────────────────────────────────────────────────────┘

Agent Prompting Philosophy

Each agent has a specific system prompt persona:

Agent	Persona
Macro Analyst	Senior macro strategist at a global macro hedge fund
Fundamental Analyst	Buy-side equity research analyst at a top-tier investment firm
Quant Analyst	Quantitative portfolio analyst at a systematic trading firm
Portfolio Strategist	Chief Investment Officer at a multi-strategy fund

Running the Agentic Pipeline

from agentic_ai.orchestrator import Orchestrator

orchestrator = Orchestrator()

# Single ticker
result = orchestrator.analyze_ticker("AAPL")

# Multiple tickers (macro cached and shared)
results = orchestrator.analyze_portfolio(["AAPL", "TSLA", "NVDA"])

---

14. Output — Email Report

Source: Email Report Dispatcher — HTML report builder and Gmail SMTP sender

The HTML email contains all sections assembled:

Subject: "Daily Stock Report: {TICKER}, {Company Name}"

HTML Email Sections:
┌──────────────────────────────────────────────────────────┐
│ 1. ML Analysis Text Report (pre-formatted text)           │
│ 2. Analysis Dashboard Chart (2×2 matplotlib grid):        │
│    ├─ [Top-Left]  Price history (6mo) + MA20/MA50         │
│    ├─ [Top-Right] LSTM 20-day price forecast               │
│    ├─ [Bot-Left]  Sentiment bar chart (News/Reddit/Score) │
│    └─ [Bot-Right] MA backtest returns vs win rates        │
│ 3. Risk vs Return Chart (vs S&P 500 scatter plot)         │
│ 4. Fundamental Data Table (Finviz HTML table)             │
│ 5. QuantStats Summary                                     │
│ 6. AI DCF Valuation Analysis                              │
│ 7. AI Final Summary (with GEX & Macro)                   │
│ 8. Macro Analysis Report                                  │
│ 9. GEX Report (Schwab option chain analysis)              │
│ 10. Covered Call Screener (optional)                      │
└──────────────────────────────────────────────────────────┘

---

15. Configuration Reference

Source: Analysis Configuration Dataclass — All tunable parameters for the ML pipeline

Quick Reference Summary

TICKER SELECTION  →  Finviz: top sector + highest Trade Value stock
FUNDAMENTAL       →  Finviz + yfinance: P/E, margins, targets, insider
DCF               →  AI model: 5yr FCF forecast → WACC → intrinsic price
ML FORECAST       →  LSTM + XGBoost: 60-day lookback → 20-day price forecast
BACKTEST          →  MA strategy (5–50): touch MA → 5% TP / -5% SL
SENTIMENT         →  FinBERT on Finviz news + Reddit (4 subreddits)
QUANT RISK        →  QuantStats: Sharpe, Sortino, VaR, CAGR + Risk/Return plot
MACRO             →  FRED: yields, CPI, unemployment + VIX + BofA signals
GEX               →  Schwab API: dealer gamma → support/resistance/flip levels
WEIGHTED SCORE    →  0.3(news) + 0.2(reddit) + 0.3(ML) + 0.2(BnH) ± macro adj
SIGNAL            →  Score > 0.5 → BUY, else HOLD
AI SUMMARY        →  All data → AI model → final recommendation with entry/exit

16. Conclusion

That’s the entire workflow from ticker selection all the way through to a final buy & hold decision with covered calls on top. Every step feeds into the next: the screener narrows the universe, fundamentals and DCF set a fair-value anchor, ML and backtesting add a forward-looking edge, sentiment captures the crowd’s mood, macro and GEX keep me aware of the bigger picture, and the weighted score ties it all together into a single actionable signal.

Is it perfect? No. Markets will always surprise you. But having a structured, repeatable process removes most of the emotion from the decision and lets the data do the heavy lifting. The agentic AI pipeline is a newer addition that gives me a second opinion, a qualitative lens on top of the quantitative one and I’ve found the two complement each other well.

Building this system has been one of the most rewarding projects I’ve worked on. If you made it this far, I hope it gave you some ideas for your own workflow. Happy investing.