// NOCTURNAL DATA INGESTION | DAWN AI ANALYSIS | 70% PREDICTIVE ACCURACY
PronoCap began as a private algorithmic experiment by Aubrey Love II. The hypothesis was simple: High-Frequency Trading is noise; Macro-Cycle Analysis is truth.
By rejecting Python in favor of a Strict PHP 8.4 JIT Architecture, we built a system that harvests massive datasets overnight ("Nocturnal Ingestion"). At dawn, this data is normalized and fed into a multi-agent AI council.
INPUT: HIGH-FIDELITY API STREAMS (COST: ~$120/MO) // OUTPUT: NORMALIZED JSON LEDGERS
We pull raw OHLCV (Open, High, Low, Close, Volume) data for the top 500 assets. Crucially, we harvest Developer Activity Scores and Market Cap Dominance to filter out "vaporware" projects. Our PHP crawler normalizes this into a SQL history table for pattern matching.
We analyze the "Blue Chip" index. Key data points include Floor Price Stability, 24h Volume Velocity, and Holder Distribution (Whale vs. Retail ratio). This allows us to predict liquidity crunches before they happen.
Coverage of NYSE/NASDAQ. We extract fundamental health metrics: P/E Ratios, EPS Growth, and Insider Trading Reports. We also map 50/200 Day Moving Averages to identify "Golden Cross" events.
PROCESS: DATA NORMALIZATION -> API INJECTION -> WEIGHTED CONSENSUS
"History doesn't repeat, but it rhymes."
Role: Technical Chart Analysis.
Integration: We convert the last 90 days of OHLCV data into a JSON array. LLaMA analyzes this vector to identify fractal patterns (e.g., Bull Flags, Head & Shoulders) that match historical breakouts.
"Markets are moved by emotion."
Role: Natural Language Processing (NLP).
Integration: Our PHP crawler scrapes headlines and news snippets relative to the asset. These text blocks are fed to FinBERT, a model trained specifically on financial texts, to determine the "Fear vs. Greed" index.
"Logic above all else."
Role: Thesis Generation.
Integration: We prompt ChatGPT to act as a "Senior Wall Street Analyst." We provide it with the technicals (RSI, MACD) and fundamentals (P/E). It must write a logical paragraph justifying a Buy or Sell decision.
"Context is king."
Role: Macro-Economic Context.
Integration: Gemini cross-references our local data with its massive knowledge graph. It looks for "Black Swan" risks (e.g., "How does the current bond yield affect this specific Tech Stock?").
"The Final Verdict."
Algorithm: Custom In-House PHP Logic.
This is the brain of PronoCap. It aggregates the outputs from all 4 AI engines.
Based on Paper Trading Logic (Swing & Value Tactics)
Visualizing the 70% Accuracy win-rate compounding over a 16-week simulated cycle.
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