Algorithmic Trading Developer

Algorithmic Trading Developer

(Execution, Risk, ML & Infrastructure)

Level: Mid-level (3–5 years hands-on algorithmic/quantitative trading experience)

Engagement: Full-time, in-house

Markets Covered: Forex (MT4/MT5) • Crypto exchange APIs • Equities and F&O broker APIs

Reports To: Head of Algo Trading / Founder

About AJAlgo 

AJALGO runs a portfolio of automated trading bots across Forex (MT4/MT5), crypto exchanges, and equity/F&O broker APIs. We build and operate systems that don't just generate trading signals, they execute them reliably, enforce risk discipline in code, and stay resilient around the clock. Our focus is on building robust, well-monitored infrastructure where every trade is accounted for and every risk limit is enforced automatically, not manually. We're a small, hands-on team where engineering rigor and calibrated scepticism matter as much as trading performance.

About the Role

We run a portfolio of automated trading bots across Forex (MT4/MT5), crypto exchanges, and equity/F&O broker APIs. The bots generate the signals; this role owns everything that happens after the signal, and everything the bots depend on to stay alive.

What You'll Own

Signal Execution & Monitoring

Own the order pipeline end to end: signal, risk checks, order routed, fill confirmed, position tracked, logged

Build and maintain a live monitoring dashboard (positions, P&L, exposure, latency, bot heartbeat)

Handle rejected orders, partial fills, requotes, slippage, disconnects, and stale prices explicitly

Run daily reconciliation between bot-held positions and broker/exchange records

Maintain a real-time alerting system with clear severity levels

Money Management & Risk Control

Implement position sizing in code: fixed-fractional risk, volatility-adjusted lots, hard caps per symbol/strategy/account

Enforce daily/weekly loss limits, drawdown stops, and margin safeguards

Track correlated exposure across bots and instruments

Guarantee every position carries a stop loss, including after restarts

Produce daily risk reports

Machine Learning on Trade History

Build clean, versioned datasets from historical trades and market context

Engineer features while avoiding look-ahead bias

Train models for signal filtering and position sizing (meta-labelling approach)

Validate using walk-forward and purged/embargoed cross-validation

Deploy with shadow, small allocation, then full allocation, with rollback plans

Monitor for model drift and define retraining triggers

Server & Trading Terminal Operations

Provision and maintain Linux VPS (bots) and Windows VPS (MT4/MT5 terminals) near broker/exchange servers

Run services under process supervision for automatic recovery

Set up uptime, resource, and heartbeat monitoring with alerts; maintain exact NTP sync

Harden access: key-based SSH, vaulted secrets, least-privilege API keys

Maintain and test backups of trade database, config, and model artefacts

Document failover and disaster-recovery runbooks

Must-Have Skills & Experience

3–5 years building/operating automated trading systems that traded real money (not just backtests)

Strong Python (pandas, NumPy, asyncio, REST/WebSocket clients); Git, structured logging, tests for money-touching code

Working knowledge of MQL4/MQL5 or driving MT5 from Python; understands magic numbers, order lifecycle, hedging vs. netting

Experience integrating at least one crypto exchange API and one broker API; rate limits, idempotent order IDs, reconciliation

Fluent in market mechanics: spread, slippage, swap/funding, margin, leverage, partial fills, rollover

Can compute correct lot size from equity, stop distance, tick value, and risk % on the spot

Practical ML experience (scikit-learn, XGBoost/LightGBM); understands overfitting, leakage, and why standard k-fold fails for time-series

Confident on Linux (SSH, systemd, cron); comfortable administering Windows VPS for MT terminals

SQL competence with trade/time-series data (PostgreSQL/TimescaleDB/SQLite)

Conservative temperament: defaults to halting and escalating rather than improvising with live capital

Good to Have

Experience at a prop firm, hedge fund, HFT desk, or funded-trader programme

Docker, CI/CD, infrastructure-as-code (Ansible/Terraform), Prometheus + Grafana

C++/C# for latency-sensitive components; FIX protocol

Familiarity with triple-barrier labelling, meta-labelling, purged K-fold with embargo

Options/F&O knowledge: Greeks, SPAN margin, expiry-day behaviour

Public repository, research write-up, or verifiable track record

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