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Tensor Capital — AI Quant Engine

Tensor Capital AI-Powered, Market-Neutral Trading Engine

Built by Luwak Technologies Pvt Ltd. Tensor Capital is an intellectual-property driven quant engine using advanced ML, volatility analytics, and low-latency execution to harvest inefficiencies across derivatives and crypto.

Section Anchor
Overview #overview
Technology #tech
Track Record #track
Industry #companies
Funding #funding
Market Inefficiency Diagram

High-Intellect, Deep-Tech Quant Business

Zero marketing burn. IP-first. Proprietary ML, market microstructure analytics, and execution science power our edge.

Predictive ML Modelling

LSTM, volatility clustering, ensemble models.

Real-Time Market Intelligence

Signal filtering, vol protection, microstructure inference.

Proprietary Execution Engine

Golang-based deterministic low-latency executor.

Technology Stack

Modular, auditable, low-latency stack for production trading.

Golang

Golang (Execution)

Low-latency order routing, concurrency and deterministic execution.

Python

Python (Models)

Model training, backtesting, feature engineering (NumPy/Pandas/scikit-learn).

Docker

Kubernetes + Docker (Infra)

Containerised deployment, autoscaling, reliability.

Kubernetes

Kubernetes (Orchestration)

Pod-level isolation, scaling, observability.

Redis

Redis (State)

Realtime state, orderbook snapshots, pub/sub streams.

Postgres

Postgres / TimescaleDB (Persistence)

Durable storage, time-series for metrics and audit logs.

Proven Live Performance

Controlled capital deployment in live markets validated our model dynamics.

Total Return (3 months)

₹50,000 (≈ $602)

Estimated conversion used: ₹83 = $1. Update for live investor materials.

Realized Profit

₹37,000 (≈ $446)

Profits realized under live trade execution; demonstrates strategy validity.

Live Market Demo Available

We can demonstrate Tensor Capital's signals, execution, and risk controls on a live exchange feed — fully transparent to invited investors.

Book Live Demo

Industry Benchmarks

Performance-driven quant firms scale without marketing spend; they reinvest in research and infra.

Jane Street

Revenue: ~$13B (2023)

Profit: ~$7.5B (2023)

Marketing Spend: $0

Renaissance Technologies

Medallion Fund: ~39% avg annual returns (after fees)

Marketing Spend: $0

Two Sigma

Revenue: ~$4–6B annually

Marketing Spend: $0

Roadmap (12–18 months)

  1. Q1: Upgrade ML models, premium feeds, team expansion.
  2. Q2: Crypto derivatives module, arbitrage engine.
  3. Q3: Scale capital, institutional outreach.
  4. Q4: AIF Category III, compliance and audits.
  5. Next Year: Global structure (VCC/DIFC/Delaware), cross-market arbitrage.

Tranche Schedule

Milestone-based monthly releases — $15k/month for 12 months; $20k reserve.

Month Amount Purpose
1 $15,000 Data feeds & infra
2 $15,000 Model deployment
3 $15,000 Execution scaling
4 $15,000 Crypto integration
5–12 $15,000/mo Arbitrage, hiring, compliance

$200,000 Raise — 10% Equity

Pre-money: $1.8M • Post-money: $2.0M