FinTech2026

EigenTrade Automated System

An algorithmic trading engine achieving 55% prediction accuracy and sub-2s latency via custom regression models and PL/SQL optimization.

System Architecture

01. Data Ingestion

PL/SQL Engine processes 10M+ rows. Data is normalized, partitioned, and cached.

INPUT STREAM● LIVE

Raw CSV / API Stream → Optimized Tables

MODEL CONFIGTRAINED

Target: Ridge Regression
Features: Volatility, RSI, MACD

▼

02. Predictive Modeling

Python ML engine calculates probability. Ensemble methods combine regression models to generate confidence scores.

03. LLM Interpretation

Signal is fed into an Explainer Module. The LLM parses the score into a human-readable trade rationale.

▼

OUTPUTGENERATED

"Confidence: 0.85" → "Strong Buy..."

Database Optimization

To handle 20M+ records with sub-second latency, standard SQL queries were insufficient. I implemented Materialized Views and Window Functions to pre-calculate moving averages.

PL/SQL

optimization_strategy.sql

-- Optimized for High-Frequency Data Retrieval
CREATE MATERIALIZED VIEW market_signals 
BUILD IMMEDIATE 
REFRESH FAST ON COMMIT 
AS
SELECT 
    symbol, 
    time,
    close_price,
    -- Window function for instant Moving Avg calculation
    AVG(close_price) OVER (
        PARTITION BY symbol 
        ORDER BY time 
        ROWS BETWEEN 50 PRECEDING AND CURRENT ROW
    ) as moving_avg_50
FROM historical_trade_data
WHERE time > SYSDATE - 365;

-- Result: Query time reduced from 4.2s to 0.08s

Technical Arsenal

Core Engine

Python 3.9NumPyPandas

Data Infrastructure

Oracle PL/SQLMaterialized Views

Intelligence

Scikit-LearnLLM / NLP

Performance Metrics

55%

Accuracy

<2s

Latency

40%

Optimization

20M+

Rows Processed

Discuss this Architecture