META PRICES 2020-2025 - Daily AI Feature Feed

What You Get

• 1,508 trading days from January 2, 2020 to December 31, 2025
• 94 columns (3 metadata + 84 features + 7 forward labels)
• Single CSV file, chronological order
• One row per trading day — clean, ready for ML ingestion

Feature Groups (94 columns)

Why Meta Sentiment Is Different

• Ad revenue cyclicality — Meta's revenue is 97%+ advertising, making it extremely sensitive to economic sentiment; our AI tracks advertiser spending narratives that predict revenue beats/misses
• Reality Labs narrative — the metaverse bet generated years of negative sentiment; tracking the shift from "money pit" to "long-term investment" narrative is critical for META valuation
• AI pivot story — Llama open-source models, AI-driven recommendation engines, and Advantage+ advertising create a new positive sentiment layer that has driven the 2023-2025 rally
• Regulatory risk — data privacy lawsuits, EU Digital Markets Act, youth safety investigations, and antitrust scrutiny create periodic negative sentiment shocks
• Platform competition — TikTok threat, Threads vs X, Instagram vs YouTube Shorts create competitive sentiment cycles captured in daily financial coverage
• Year of Efficiency narrative — the 2023 cost-cutting story transformed META sentiment from universally bearish to the most-loved FAANG stock

Collection Methodology

1. News Collection: GDELT (Global Database of Events, Language, and Tone) provides 100+ Meta-related articles daily from global financial media
2. AI Sentiment Analysis: Google Gemini processes each article batch, scoring overall market sentiment and META-specific sentiment on a -1.0 to +1.0 scale
3. Price Data: Yahoo Finance provides META closing prices, S&P 100 cross-asset data, sector returns, VIX, yields, credit spreads, and options data
4. Feature Engineering: 91 features are computed from raw inputs using numpy — technical indicators, sentiment derivatives, cross-asset correlations, macro signals, and forward labels
5. Quality Control: Automated audit checks coverage, NaN rates, column integrity, and data consistency

Complete Column Reference

# | Column | Type | Group | Description
---|--------|------|-------|------------
1 | date | date | Metadata | Trading date (YYYY-MM-DD)
2 | year | int | Metadata | Calendar year
3 | day_of_week | string | Metadata | Day name (Monday-Friday)
4 | price_close | float | Price Action | Closing price (USD)
5 | price_return_1d | float | Price Action | 1-day return (%)
6 | price_return_3d | float | Price Action | 3-day return (%)
7 | price_return_5d | float | Price Action | 5-day return (%)
8 | price_return_10d | float | Price Action | 10-day return (%)
9 | price_return_20d | float | Price Action | 20-day return (%)
10 | price_log_return_1d | float | Price Action | 1-day log return
11 | price_dist_high_10d | float | Price Action | Distance from 10-day high (%)
12 | price_dist_high_20d | float | Price Action | Distance from 20-day high (%)
13 | price_dist_low_10d | float | Price Action | Distance from 10-day low (%)
14 | price_dist_low_20d | float | Price Action | Distance from 20-day low (%)
15 | tech_rsi_14 | float | Technical | RSI 14-day (0-100, >70 overbought, <30 oversold)
16 | tech_sma_5 | float | Technical | 5-day Simple Moving Average (USD)
17 | tech_sma_20 | float | Technical | 20-day Simple Moving Average (USD)
18 | tech_sma_5_dist | float | Technical | Distance from 5-day SMA (%)
19 | tech_sma_20_dist | float | Technical | Distance from 20-day SMA (%)
20 | tech_ema_20 | float | Technical | 20-day Exponential Moving Average (USD)
21 | tech_ema_20_dist | float | Technical | Distance from 20-day EMA (%)
22 | tech_bollinger_pos | float | Technical | Position within Bollinger Bands (0=lower, 0.5=middle, 1=upper)
23 | tech_bollinger_bw | float | Technical | Bollinger Band width (%)
24 | tech_bollinger_squeeze | float | Technical | Squeeze indicator (1 = bandwidth in bottom 10th percentile, upcoming breakout)
25 | tech_macd | float | Technical | MACD line
26 | tech_macd_signal | float | Technical | MACD signal line
27 | tech_macd_hist | float | Technical | MACD histogram (positive = bullish momentum)
28 | tech_adx | float | Technical | ADX trend strength (>25 trending, <20 ranging)
29 | tech_roc_5 | float | Technical | 5-day Rate of Change (%)
30 | tech_roc_10 | float | Technical | 10-day Rate of Change (%)
31 | tech_roc_20 | float | Technical | 20-day Rate of Change (%)
32 | tech_streak | int | Technical | Consecutive up/down days (positive = up streak)
33 | tech_vol_5d | float | Technical | 5-day realized volatility (annualized %)
34 | tech_vol_10d | float | Technical | 10-day realized volatility (%)
35 | tech_vol_20d | float | Technical | 20-day realized volatility (%)
36 | tech_momentum_quality | float | Technical | Momentum consistency score (ROC adjusted for volatility)
37 | sent_overall | float | AI Sentiment | Overall market sentiment (-1.0 bearish to +1.0 bullish)
38 | sent_meta | float | AI Sentiment | Meta-specific sentiment (-1.0 bearish to +1.0 bullish)
39 | sent_spread | float | AI Sentiment | Ticker minus overall sentiment (positive = stock more bullish than market)
40 | sent_overall_ma3 | float | AI Sentiment | 3-day MA of overall sentiment
41 | sent_meta_ma3 | float | AI Sentiment | 3-day MA of meta sentiment
42 | sent_overall_ma5 | float | AI Sentiment | 5-day MA of overall sentiment
43 | sent_meta_ma5 | float | AI Sentiment | 5-day MA of meta sentiment
44 | sent_overall_mom3 | float | AI Sentiment | 3-day overall sentiment momentum
45 | sent_meta_mom3 | float | AI Sentiment | 3-day meta sentiment momentum
46 | sent_overall_mom5 | float | AI Sentiment | 5-day overall sentiment momentum
47 | sent_meta_mom5 | float | AI Sentiment | 5-day meta sentiment momentum
48 | sent_overall_vol5 | float | AI Sentiment | 5-day overall sentiment volatility
49 | sent_overall_vol10 | float | AI Sentiment | 10-day overall sentiment volatility
50 | sent_meta_vol5 | float | AI Sentiment | 5-day meta sentiment volatility
51 | sent_price_corr_10d | float | Sent x Price | 10-day rolling sentiment-price correlation
52 | sent_price_corr_20d | float | Sent x Price | 20-day rolling sentiment-price correlation
53 | sent_price_diverge | float | Sent x Price | Divergence flag (1 when sentiment and price disagree 3+ consecutive days)
54 | news_count | int | News Volume | Articles collected that day
55 | news_count_ma20 | float | News Volume | 20-day article count moving average
56 | news_count_zscore | float | News Volume | Z-score vs 20-day window (>2 = unusual volume)
57 | news_count_mom5 | float | News Volume | 5-day article count momentum
58 | news_spike | float | News Volume | Binary flag for abnormal news volume (count > 2x MA)
59 | mkt_sp100_breadth | float | Cross-Asset | S&P 100 market breadth (% of stocks up, 0-100)
60 | mkt_sp100_return | float | Cross-Asset | S&P 100 equal-weight return (%)
61 | mkt_dispersion | float | Cross-Asset | Cross-sectional return dispersion (%)
62 | mkt_tech_return | float | Cross-Asset | Tech sector return (%)
63 | mkt_finance_return | float | Cross-Asset | Finance sector return (%)
64 | mkt_health_return | float | Cross-Asset | Healthcare sector return (%)
65 | mkt_energy_return | float | Cross-Asset | Energy sector return (%)
66 | mkt_consumer_return | float | Cross-Asset | Consumer sector return (%)
67 | mkt_industrial_return | float | Cross-Asset | Industrial sector return (%)
68 | mkt_meta_vs_tech | float | Cross-Asset | Meta minus tech sector return (%)
69 | mkt_meta_vs_market | float | Cross-Asset | Meta minus S&P 100 return (%)
70 | mkt_meta_beta_20d | float | Cross-Asset | 20-day rolling beta vs S&P 100
71 | vol_of_vol_20d | float | Vol Regime | Volatility of volatility (20-day rolling)
72 | vol_regime | int | Vol Regime | Regime: 0=low, 1=normal, 2=high, 3=extreme
73 | sent_vol_regime_interaction | float | Interaction | Meta sentiment x volatility regime (amplified signal in high-vol periods)
74 | macro_vix | float | Macro | VIX level (CBOE Volatility Index)
75 | macro_vix_change_1d | float | Macro | 1-day VIX percentage change
76 | macro_vix_ma5 | float | Macro | VIX 5-day moving average
77 | macro_yield_spread | float | Macro | 10Y minus short-term Treasury yield spread (%)
78 | macro_credit_spread | float | Macro | High-yield credit spread proxy (LQD/HYG ratio)
79 | macro_dxy_change | float | Macro | US Dollar Index 1-day percentage change (%)
80 | options_iv_atm | float | Options | ATM implied volatility (%) via VIX x beta approximation
81 | options_iv_rv_spread | float | Options | IV minus realized vol (positive = fear premium)
82 | earnings_days_to_next | float | Earnings | Days to next quarterly earnings (0 = earnings day, capped at 90)
83 | earnings_is_earnings_day | float | Earnings | Binary flag: 1 on earnings day, 0 otherwise
84 | attention_wikipedia_views | float | Social Attention | Daily Wikipedia pageviews (1-day lagged)
85 | attention_wikipedia_zscore | float | Social Attention | Wikipedia views z-score (>2 = unusual interest)
86 | attention_google_trends | float | Social Attention | Google Trends index (0-100)
87 | attention_google_trends_change | float | Social Attention | Google Trends change vs prior period
88 | label_return_1d | float | Forward Labels | Next-day return (%)
89 | label_dir_1d | string | Forward Labels | Next-day direction (UP/DOWN/FLAT)
90 | label_return_3d | float | Forward Labels | 3-day forward return (%)
91 | label_dir_3d | string | Forward Labels | 3-day forward direction
92 | label_return_5d | float | Forward Labels | 5-day forward return (%)
93 | label_dir_5d | string | Forward Labels | 5-day forward direction
94 | label_flat_1d | float | Forward Labels | Flat flag (1 if next-day |return| < 0.3%)

Data Quality

• NaN values limited to first ~30 rows (indicator warmup period for rolling windows)
• Last 1-5 rows may have empty forward labels (not yet realized)
• Zero gaps in sentiment coverage — every trading day has article data
• Cross-asset features sourced from Yahoo Finance; occasional NaN on mismatched trading holidays
• Quality rating: 5/5 (automated audit verified)

Known Limitations

• Meta-specific sentiment (sent_meta) is derived from AI analysis of English-language financial news via GDELT. Social media sentiment (Instagram, Threads, Reddit) is not directly included.
• Options IV (options_iv_atm) is approximated using VIX x META beta, not from actual META options chains. Given META's history of 20%+ earnings day moves, this approximation significantly understates event IV.
• The Facebook-to-Meta rebrand (October 2021) and ticker change (June 2022) may create slight discontinuities in sentiment signal naming in older news sources.
• Earnings dates are pattern-estimated with yfinance confirmation. Accuracy is +/- 3 days for some historical quarters.
• META's 77% drawdown in 2022 and 400%+ recovery in 2023-24 represent extreme regime changes — models should incorporate volatility regime features for robust cross-period performance.

Use Cases

• ML model training for META price direction prediction — one of the strongest sentiment-price relationships among mega-caps
• Corporate reinvention analysis: quantifying how narrative shifts (metaverse → AI) drive multi-hundred-percent repricing
• Ad revenue cycle modeling: sentiment as a leading indicator for digital advertising spending trends
• Regulatory event study: ATT privacy shock, EU DMA, antitrust actions — sentiment impact and recovery patterns
• Competitive dynamics analysis: TikTok threat narrative, Threads launch reception, Instagram engagement trends
• Feature engineering baseline for digital advertising or social media sector strategies
• Earnings surprise prediction using pre-earnings ad revenue sentiment and Reality Labs spending narrative shifts

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