AI-Based Algorithmic Trading System Development
Project Overview
I am looking for an experienced developer with strong expertise in algorithmic trading, machine learning, and financial markets to build an AI-driven automated trading system.
The system must be able to:
analyze financial markets
discover profitable trading strategies
perform extensive historical backtesting
test strategies in demo conditions
execute trades automatically in live markets
continuously improve its performance through learning.
The goal is to create a robust and adaptive automated trading system capable of evolving with changing market conditions.
Markets to Trade
The AI must operate on the following futures markets:
Nasdaq-100 (NQ)
S&P 500 (ES)
Dow Jones (YM)
Gold (GC)
Crude Oil WTI (CL)
US 10Y Treasury (ZN)
US 30Y Treasury (ZB)
⚠️ Important requirement:
One strategy must be developed per market.
A strategy that works well on one market may not work on another due to differences in volatility, liquidity, and market behavior.
Project Phases
Phase 1 — Advanced Backtesting
The AI must:
test a wide range of trading strategies
analyze technical indicators
analyze chart patterns
analyze candlestick formations
analyze macroeconomic events.
Backtesting must be performed on multiple historical periods:
10 years
5 years
3 years
1 year
1 month
Backtests must include:
commissions
slippage
spreads
different DMA brokers.
Phase 2 — Demo Testing
After selecting the best strategies:
test them in demo trading or micro contracts
starting capital: €2000
maximum risk per trade: 5%
stop loss: 15% of the position size
Performance metrics must include:
drawdown
win rate
profit factor
trade frequency.
Phase 3 — Live Trading
Once validated:
automated execution
dynamic position sizing
reinvestment of profits
ongoing monitoring and optimization.
Features the AI Must Include
The AI must analyze:
Technical Indicators
Examples include:
RSI
MACD
Moving averages
Bollinger Bands
ATR
Ichimoku
and others.
Chart Patterns
Examples include:
triangles
double top / double bottom
head and shoulders
channels
breakout structures.
Candlestick Patterns
Examples include:
doji
engulfing
hammer
shooting star.
Order Flow and Market Microstructure
The AI must analyze:
order flow
order book (DOM)
volume profile
liquidity zones.
Market Session Analysis
The AI must incorporate global market sessions.
Asian Session
The system must analyze what happened overnight during the Asian trading session and determine how it impacts European and US markets.
London Open
The AI must learn the impact of the London session on volatility and market direction.
US Market Open
The system should identify the most profitable opportunities during the US trading session.
Risk Management
The AI must include advanced risk management features:
dynamic position sizing
adaptive stop losses
trailing stops
dynamic take profit management.
The goal is to maximize profit aggressively but in a controlled and risk-managed way.
Machine Learning and Self-Improvement
The system must be capable of:
analyzing every trade
understanding why trades succeed
understanding why trades fail
improving strategy parameters over time.
The AI must avoid overfitting and focus on robust strategies that perform well across different market conditions.
Brokers
The system must work with DMA brokers only.
Examples include:
Interactive Brokers
Tradovate
AMP Futures
(no prop firms).
Required Skills
The ideal developer should have experience in:
algorithmic trading
Python
machine learning
backtesting frameworks
broker APIs
financial markets.
Experience with the following is a strong plus:
QuantConnect
Backtrader
Zipline
NinjaTrader
MetaTrader.
Deliverables
Expected deliverables include:
full source code
documentation
backtesting results
monitoring dashboard
broker API integration
self-learning components.
Apply Now
Apply Now
I am looking for an experienced developer with strong expertise in algorithmic trading, machine learning, and financial markets to build an AI-driven automated trading system.
The system must be able to:
analyze financial markets
discover profitable trading strategies
perform extensive historical backtesting
test strategies in demo conditions
execute trades automatically in live markets
continuously improve its performance through learning.
The goal is to create a robust and adaptive automated trading system capable of evolving with changing market conditions.
Markets to Trade
The AI must operate on the following futures markets:
Nasdaq-100 (NQ)
S&P 500 (ES)
Dow Jones (YM)
Gold (GC)
Crude Oil WTI (CL)
US 10Y Treasury (ZN)
US 30Y Treasury (ZB)
⚠️ Important requirement:
One strategy must be developed per market.
A strategy that works well on one market may not work on another due to differences in volatility, liquidity, and market behavior.
Project Phases
Phase 1 — Advanced Backtesting
The AI must:
test a wide range of trading strategies
analyze technical indicators
analyze chart patterns
analyze candlestick formations
analyze macroeconomic events.
Backtesting must be performed on multiple historical periods:
10 years
5 years
3 years
1 year
1 month
Backtests must include:
commissions
slippage
spreads
different DMA brokers.
Phase 2 — Demo Testing
After selecting the best strategies:
test them in demo trading or micro contracts
starting capital: €2000
maximum risk per trade: 5%
stop loss: 15% of the position size
Performance metrics must include:
drawdown
win rate
profit factor
trade frequency.
Phase 3 — Live Trading
Once validated:
automated execution
dynamic position sizing
reinvestment of profits
ongoing monitoring and optimization.
Features the AI Must Include
The AI must analyze:
Technical Indicators
Examples include:
RSI
MACD
Moving averages
Bollinger Bands
ATR
Ichimoku
and others.
Chart Patterns
Examples include:
triangles
double top / double bottom
head and shoulders
channels
breakout structures.
Candlestick Patterns
Examples include:
doji
engulfing
hammer
shooting star.
Order Flow and Market Microstructure
The AI must analyze:
order flow
order book (DOM)
volume profile
liquidity zones.
Market Session Analysis
The AI must incorporate global market sessions.
Asian Session
The system must analyze what happened overnight during the Asian trading session and determine how it impacts European and US markets.
London Open
The AI must learn the impact of the London session on volatility and market direction.
US Market Open
The system should identify the most profitable opportunities during the US trading session.
Risk Management
The AI must include advanced risk management features:
dynamic position sizing
adaptive stop losses
trailing stops
dynamic take profit management.
The goal is to maximize profit aggressively but in a controlled and risk-managed way.
Machine Learning and Self-Improvement
The system must be capable of:
analyzing every trade
understanding why trades succeed
understanding why trades fail
improving strategy parameters over time.
The AI must avoid overfitting and focus on robust strategies that perform well across different market conditions.
Brokers
The system must work with DMA brokers only.
Examples include:
Interactive Brokers
Tradovate
AMP Futures
(no prop firms).
Required Skills
The ideal developer should have experience in:
algorithmic trading
Python
machine learning
backtesting frameworks
broker APIs
financial markets.
Experience with the following is a strong plus:
QuantConnect
Backtrader
Zipline
NinjaTrader
MetaTrader.
Deliverables
Expected deliverables include:
full source code
documentation
backtesting results
monitoring dashboard
broker API integration
self-learning components.
Apply Now
Apply Now