AI / Quant Trading Systems Developer – Python, AI Agents & Swing Trading

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: TYPE OF WORK

Part Time

: SALARY

10-20 hourly

: HOURS PER WEEK

15

: DATE UPDATED

Sep 28, 2026

JOB OVERVIEW

## About the Project

I am looking for a highly capable Python / AI developer to help architect and build a private AI-powered investment research, swing-trading, event-detection, portfolio-management, backtesting, and risk-management platform.

This is NOT a virtual assistant position.

This is NOT simply an OpenAI/ChatGPT prompt-engineering position.

And I am NOT looking for someone to build a generic “AI trading bot” that immediately trades real money.

I am looking for a real developer who understands software engineering, APIs, financial-market data, AI/LLM integration, databases, automation, backtesting, and ideally algorithmic or systematic trading.

The initial project will focus on research, scanning, backtesting, paper trading, risk management, and decision support.

Live trading would only be considered much later and would require human approval and strict risk controls.

---

## WHAT WE WANT TO BUILD

The long-term goal is a private **AI Investment & Trading Command Center** containing several specialized systems.

### 1. Swing Trading Scanner

Continuously scan a defined universe of stocks for high-quality swing-trading setups based on measurable criteria such as:

- Price and volume
- Relative volume
- VWAP
- Moving averages
- Relative strength
- Breakouts/retests
- Support/resistance
- Volatility
- Liquidity
- Market regime
- Upcoming catalysts
- Risk/reward

The system should rank opportunities rather than simply produce hundreds of alerts.

---

### 2. Event / Catalyst Detection Engine

We want a specialized Event Radar capable of detecting potentially market-moving developments including:

- Breaking company news
- SEC filings
- Earnings and guidance
- FDA decisions
- Clinical-trial results
- Biotech catalysts
- M&A activity
- Major partnerships
- Government contracts
- Regulatory decisions
- Litigation developments
- Analyst changes
- Other events capable of materially repricing a security

The system should evaluate not only whether news sounds positive or negative, but:

- How significant the event is
- Whether the information is actually new
- Source reliability
- What the market expected beforehand
- Price/volume reaction after the event
- Whether the opportunity remains tradable or is already excessively extended

Primary-source information should be prioritized whenever possible.

---

## 3. AI Research Agent

Use LLMs to help interpret information rather than relying on AI for raw calculations.

The research system should be capable of:

- Reading SEC filings
- Reading company announcements
- Summarizing earnings
- Interpreting catalysts
- Comparing new information with previous disclosures
- Identifying bull and bear arguments
- Detecting contradictory information
- Identifying important risks
- Citing the underlying evidence/source

The system should distinguish confirmed information from rumors or low-confidence information.

AI should NOT invent market data or calculate important risk values itself.

Prices, volume, indicators, portfolio exposure, position sizing, P&L and similar calculations should be performed deterministically in Python from trusted data sources.

---

## 4. Trade Scoring System

Potential opportunities should receive transparent component scores rather than an unexplained AI recommendation.

Possible components include:

- Catalyst score
- Technical score
- Price/volume confirmation
- Liquidity
- Market regime
- Options/institutional confirmation
- Risk
- Overall setup quality

Every calculation and weighting should be auditable.

We eventually want to test whether each scoring component actually improves trading performance.

---

## 5. HARD RISK MANAGEMENT ENGINE

This is one of the most important parts of the project.

Risk management should be deterministic software — NOT controlled by the language model.

The system should eventually be capable of enforcing rules involving:

- Maximum risk per trade
- Position sizing
- Maximum daily/weekly losses
- Maximum drawdown
- Portfolio concentration
- Sector exposure
- Correlated positions
- Maximum number of simultaneous positions
- Liquidity requirements
- Maximum bid/ask spreads
- Minimum risk/reward
- Stale-data protection
- Kill switches

The risk engine must be capable of rejecting a trade even if the AI believes the opportunity is attractive.

---

## 6. Portfolio Management

Build a Master Portfolio dashboard capable of monitoring:

- Holdings
- Cost basis
- Market value
- Allocation
- Cash
- Gains/losses
- Dividend income
- Portfolio concentration
- Risk exposure
- Open swing trades
- Watchlists
- Upcoming portfolio catalysts

Initial portfolio data may be entered manually or imported.

Brokerage integration may be added later using secure official APIs.

No developer will receive brokerage usernames/passwords.

---

## 7. Trade Pipeline

Potential trades should progress through a structured workflow such as:

RESEARCH
→ WATCHLIST
→ SETUP
→ READY
→ ACTIVE
→ CLOSED

Each opportunity should store:

- Ticker
- Timestamp
- Original price
- Catalyst
- Thesis
- Bull case
- Bear case
- Entry
- Stop/invalidation
- Target
- Risk/reward
- Position size
- Scores
- Sources
- Notes
- Outcome

---

## 8. Backtesting & Historical Replay

We want serious testing — not backtests designed to produce attractive screenshots.

The system should account for issues such as:

- Look-ahead bias
- Survivorship bias
- Delisted securities
- Corporate actions
- Historical point-in-time information
- Bid/ask spreads
- Slippage
- Transaction costs
- Realistic fills

A future capability should allow historical market days/events to be replayed chronologically without allowing the strategy to see future information.

---

## 9. Paper Trading / Shadow Mode

Before real capital is involved, the system should:

1. Generate signals live without trading
2. Record what would have happened
3. Paper trade qualifying strategies
4. Track results
5. Evaluate performance statistically

We want to measure metrics including:

- Expectancy
- Win rate
- Average winner
- Average loser
- Profit factor
- Maximum drawdown
- Risk-adjusted returns
- Performance by strategy
- Performance by catalyst type
- Performance by market regime
- Slippage
- False positives

---

## 10. Trading Journal & Learning Database

Every signal should be recorded — including trades we reject or choose not to take.

Over time we want to answer questions such as:

- Which setups produce the highest expectancy?
- Which catalyst types perform best?
- Does unusual options activity improve outcomes?
- Does waiting for VWAP confirmation improve performance?
- Which market environments hurt the strategy?
- Which scoring components actually add value?
- Does human intervention improve or hurt performance?

This database will become one of the most valuable parts of the system.

---

## 11. Daily Investment Brief

Eventually the platform should automatically generate a daily report containing:

- Portfolio status
- Important developments affecting holdings
- Overnight news
- Market environment
- Upcoming catalysts
- Watchlist changes
- Top swing-trade candidates
- High-impact event alerts
- Existing position updates
- Portfolio risk alerts
- Trades requiring review

It should also be perfectly capable of reporting:

**NO QUALIFYING SETUPS TODAY**

The system should never manufacture trades just because the user expects recommendations.

---

# EXPECTED TECHNOLOGY

Strong experience with several of the following is important:

- Python
- Pandas / NumPy
- FastAPI
- REST APIs
- WebSockets
- PostgreSQL
- Financial-market APIs
- Market-data APIs
- AI / LLM APIs
- OpenAI / Claude / similar models
- AI agents
- Git / GitHub
- Cloud deployment
- Secure API credential management
- Background workers / scheduled jobs
- Backtesting systems
- Data pipelines

Experience with any of the following is particularly valuable:

- Algorithmic trading
- Quantitative trading
- Systematic trading
- Stock-market data
- Options data
- Brokerage APIs
- SEC/EDGAR data
- TradingView
- Alpaca
- Interactive Brokers
- Schwab APIs
- Polygon/Massive
- Benzinga
- Financial news APIs

---

# SECURITY & DEVELOPMENT PRINCIPLES

The developer must understand that financial software requires significantly stronger controls than a normal automation project.

We expect:

- Clean source control
- Documentation
- Testing
- Logging
- Error handling
- Database backups
- Secrets management
- Least-privilege API permissions
- Development/test/production separation
- Auditable calculations
- Reproducible deployments

The developer will work inside infrastructure/accounts controlled by the project owner.

Credentials should not be hard-coded into source code.

---

# PHASE 1

We are NOT attempting to build everything above immediately.

The initial goal is to design the correct architecture and build an MVP.

Phase 1 will likely include:

- Market-data ingestion
- Portfolio/watchlist database
- Initial swing scanner
- Initial catalyst/news ingestion
- AI research workflow
- Transparent scoring system
- Deterministic risk calculations
- Trade pipeline
- Paper/shadow trading
- Performance database
- Basic private dashboard

If Phase 1 is successful, there is substantial opportunity for ongoing development.

---

# WHO I AM LOOKING FOR

The ideal candidate is:

- An actual software developer
- Strong with Python
- Comfortable with APIs and databases
- Able to architect systems rather than simply follow tutorials
- Familiar with financial markets
- Familiar with systematic or algorithmic trading concepts
- Familiar with LLM/AI integration
- Comfortable explaining technical decisions to a non-developer
- Security conscious
- Careful with data
- Interested in testing assumptions rather than promising guaranteed returns

I value demonstrated ability more than degrees or certifications.

I would rather hire someone who tells me an idea is technically or statistically flawed than someone who simply agrees with everything I suggest.

---

# IMPORTANT

I am NOT looking for anyone promising guaranteed returns.

I am NOT looking for a black-box trading strategy.

I am NOT asking someone to immediately connect an AI model to a brokerage account and trade real money.

The objective is to build a measurable, auditable and progressively tested investment/trading research platform.

---

# HOW TO APPLY

Begin your application with the exact words:

**EVENT ENGINE**

Then answer the following:

1. Describe the most sophisticated Python system you personally built.

2. Have you built an algorithmic trading, quantitative, backtesting, financial-market or brokerage-related application? Describe exactly what you built.

3. Which market-data or brokerage APIs have you personally integrated?

4. Explain one common mistake that can make a trading backtest appear profitable when the strategy would not actually have performed that way live.

5. If breaking Phase 3 clinical-trial results caused a stock to move dramatically in premarket trading, how would you architect a system to detect, verify, analyze and monitor the event?

6. How would you prevent an LLM from hallucinating a price, position size or other financially important calculation?

7. How would you securely store API/broker credentials?

8. Provide links to your GitHub, code samples, portfolio, or previous relevant work.

9. Provide your requested hourly rate and weekly availability.

Please answer the questions directly.

Generic AI-generated applications without specific examples of your own work will not be considered.

Strong applicants may be invited to complete a small PAID technical evaluation before being selected.

SKILL REQUIREMENT

Python Stock Trading AI Automation

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AI / Quant Trading Systems Developer – Python, AI Agents & Swing Trading

Please login or register as jobseeker to apply for this job.

TYPE OF WORK

SALARY

HOURS PER WEEK

DATE UPDATED

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