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{{Ownership|primary=Rikk|secondary=Pawan}} | {{Ownership|primary=Rikk|secondary=Pawan}} | ||
* [[Experimentation | * [[Experimentation SOP]] — Research design & evaluation methodology | ||
* [[Market Regimes]] — Market state classification & behavior | * [[Market Regimes]] — Market state classification & behavior | ||
* [[Alpha Research]] — Strategy & signal research | * [[Alpha Research]] — Strategy & signal research | ||
* [[Failure Analysis]] — Post-analysis of drawdowns & breakdowns | * [[Failure Analysis]] — Post-analysis of drawdowns & breakdowns | ||
=== Products & Systems === | === Products & Systems === | ||
Revision as of 17:18, 28 December 2025
Welcome to the PlusEV Wiki
This wiki serves as the central knowledge base for PlusEV AI Quant Trading Private Limited. It documents our research frameworks, systems, engineering, processes & operational knowledge.
Current Scope
- Research systems covering historical backtesting and live strategy execution
- Quantitative frameworks focused on risk-first trading
- Internal SOPs for experimentation, validation, and deployment
- Data pipelines and analytics infrastructure
Core Areas
Research
Ownership: Primary — Rikk · Secondary — Pawan
- Experimentation SOP — Research design & evaluation methodology
- Market Regimes — Market state classification & behavior
- Alpha Research — Strategy & signal research
- Failure Analysis — Post-analysis of drawdowns & breakdowns
Products & Systems
Ownership: Primary — Pawan · Secondary — Rikk
BacktestIQ — Strategy Simulation Platform
Purpose: Historical strategy evaluation & simulation
- Problem
Traders develop strategies but lack visibility into how they would have performed historically, leading to uncertainty and unmanaged risk.
- Solution
BacktestIQ simulates trading strategies on historical market data, enabling traders to evaluate performance across months or years before deploying real capital.
- Who benefits
Traders, investors, funds, researchers validating strategy ideas prior to live implementation.
- Related systems
Backtesting Engine, Experimentation Framework
SignalAI — AI-Powered Decision Support
Purpose: Data-driven trade signal generation
- Problem
Real-time trading decisions are often emotional and inconsistent due to cognitive overload.
- Solution
SignalAI analyzes multiple market variables simultaneously to generate quantitatively backed buy/sell signals, reducing emotional bias.
- Who benefits
Traders seeking systematic, rule-based decision support.
- Related systems
Alpha Research, Market Regimes
TradeAnalyzer — Risk & Performance Analysis
Purpose: Risk management and performance diagnostics
- Problem
Poor position sizing and lack of performance analysis lead to severe drawdowns and capital erosion.
- Solution
TradeAnalyzer evaluates historical trading performance and guides future position sizing and risk limits.
- Who benefits
Traders aiming to improve discipline, risk control, and long-term consistency.
- Related systems
Risk Engine, Failure Analysis
StrategyLive — Automated Execution
Purpose: Strategy automation & live execution
- Problem
Manual strategy execution is error-prone, slow, and difficult to scale.
- Solution
StrategyLive automates execution of validated strategies with real-time monitoring and controls.
- Who benefits
Traders seeking hands-free, rules-based strategy deployment.
- Related systems
Live Trading System, Risk Engine
Engineering
- System Architecture
- Data Ingestion
- Feature Engineering
- Model Training
- Deployment Pipelines
- Incident Playbooks