Advanced AI Trading Bot Development
Budget: £250 – £750 GBP
Here is a precise and developer-ready specification of the bot’s architecture and all functionalities — in English — so that a developer (C++/Python/AI specialist) can clearly understand and implement it step by step.
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KI Trading Bot — Developer Specification
1. General Purpose
The bot is a fully autonomous, self-learning AI-based trading system that:
Executes trades via FIX API
Receives real-time market data (tick + level 2)
Trains & adapts its strategy over time using Reinforcement Learning
Supports multi-asset trading
Optimizes orders, hedging, SL/TP, and position sizing in real time
Detects smart money movements & market manipulation
Operates in both backtest, simulation, and live modes
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2. Architecture Overview
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3. Market Access
FIX API Live
Market Data Stream: port 5211
Trade Execution Stream: port 5212
Assets
Forex Majors & Crosses
Indices: US30, NAS100, SPX500, DAX40
Commodities: Gold, Silver, Oil
Crypto: BTC, ETH, EOS
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4. Core Functions (Grouped)
4.1 AI Strategy Engine (Python)
Multiple AI models working in parallel:
Trend Following (Transformer, LSTM)
Mean Reversion (Autoencoder + XGBoost)
Scalping (Reinforcement Learning with PPO/DQN)
Breakout Detection (CNN + LSTM)
Ensemble Learning selects best strategy based on market phase
4.2 Adaptive Trade Execution (C++)
Smart Order Selection: Market, Limit, Iceberg, Dark Pool routing
Order modification & cancellation
Real-time latency & slippage measurement
Auto-adaptive Grid + Martingale system controlled by AI
4.3 Real-Time Risk Management
AI-monitored margin usage
Adaptive SL/TP levels based on ATR, Volatility & Order Flow
Drawdown protection with auto-hedging (full/partial/cross-asset)
Crash detection & exit strategies for Black Swan events
4.4 Backtesting & Live Simulation
Tick-level backtests using 2003–2025 data
Parallel strategy testing via VectorBT + Backtrader
Strategy learning from live FIX feed (Online RL)
Black Swan events included in simulation
4.5 Market Analysis & Data Feeds
Real-time Level-2 Order Book via FIX
Dark Pool detection via clustering and pattern recognition
Sentiment Analysis via:
Twitter API, News API, Bloomberg RSS
Hugging Face (BERT, FinBERT) for NLP
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5. Execution Flow
Python (AI Logic)
signal = ai_strategy.predict(current_market_state)
if signal == 'buy':
send_order_to_cpp("BUY", symbol, size, sl, tp)
elif signal == 'hedge':
send_hedge_order("SELL", correlated_asset, size)
C++ (FIX Layer)
Builds FIX NewOrderSingle message
Sends via QuickFIX to IC Markets
Receives ExecutionReport
Sends result via ZeroMQ back to Python
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6. Data & Storage Design
Historical data: Parquet files per symbol with tick granularity
Live data & trades: SQLite + PostgreSQL
AI training data: Stored in /ai/data/ folder with versioning
Symbol metadata, order book snapshots, and logs are archived
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7. AI Training Phases
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8. Reward System (AI Evaluation)
Each AI model is rewarded based on:
Net Profit per trade
Slippage avoidance
Drawdown reduction
Spread/commission cost minimization
SL/TP efficiency
Proper use of hedging when volatility increases
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9. Developer Notes
Execution Code (C++): Lives in C:\KIBotProjekt\fix_api_cpp
AI Code (Python): Lives in C:\KIBotProjekt\ai_core
Backtesting Data: C:\KIBotProjekt\data\backtests\
All code must be modular (each AI strategy is a class/module)
Logging and debugging enabled in both Python and C++ side
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10. Optional Enhancements (Planned)
GPU acceleration with ONNX + TensorRT
Auto-strategy evolution with Genetic AI
Visual dashboard using Plotly + FastAPI
Execution fallback if FIX latency too high
Kafka for distributed AI agent deployment
---
KI Trading Bot — Developer Specification
1. General Purpose
The bot is a fully autonomous, self-learning AI-based trading system that:
Executes trades via FIX API
Receives real-time market data (tick + level 2)
Trains & adapts its strategy over time using Reinforcement Learning
Supports multi-asset trading
Optimizes orders, hedging, SL/TP, and position sizing in real time
Detects smart money movements & market manipulation
Operates in both backtest, simulation, and live modes
---
2. Architecture Overview
---
3. Market Access
FIX API Live
Market Data Stream: port 5211
Trade Execution Stream: port 5212
Assets
Forex Majors & Crosses
Indices: US30, NAS100, SPX500, DAX40
Commodities: Gold, Silver, Oil
Crypto: BTC, ETH, EOS
---
4. Core Functions (Grouped)
4.1 AI Strategy Engine (Python)
Multiple AI models working in parallel:
Trend Following (Transformer, LSTM)
Mean Reversion (Autoencoder + XGBoost)
Scalping (Reinforcement Learning with PPO/DQN)
Breakout Detection (CNN + LSTM)
Ensemble Learning selects best strategy based on market phase
4.2 Adaptive Trade Execution (C++)
Smart Order Selection: Market, Limit, Iceberg, Dark Pool routing
Order modification & cancellation
Real-time latency & slippage measurement
Auto-adaptive Grid + Martingale system controlled by AI
4.3 Real-Time Risk Management
AI-monitored margin usage
Adaptive SL/TP levels based on ATR, Volatility & Order Flow
Drawdown protection with auto-hedging (full/partial/cross-asset)
Crash detection & exit strategies for Black Swan events
4.4 Backtesting & Live Simulation
Tick-level backtests using 2003–2025 data
Parallel strategy testing via VectorBT + Backtrader
Strategy learning from live FIX feed (Online RL)
Black Swan events included in simulation
4.5 Market Analysis & Data Feeds
Real-time Level-2 Order Book via FIX
Dark Pool detection via clustering and pattern recognition
Sentiment Analysis via:
Twitter API, News API, Bloomberg RSS
Hugging Face (BERT, FinBERT) for NLP
---
5. Execution Flow
Python (AI Logic)
signal = ai_strategy.predict(current_market_state)
if signal == 'buy':
send_order_to_cpp("BUY", symbol, size, sl, tp)
elif signal == 'hedge':
send_hedge_order("SELL", correlated_asset, size)
C++ (FIX Layer)
Builds FIX NewOrderSingle message
Sends via QuickFIX to IC Markets
Receives ExecutionReport
Sends result via ZeroMQ back to Python
---
6. Data & Storage Design
Historical data: Parquet files per symbol with tick granularity
Live data & trades: SQLite + PostgreSQL
AI training data: Stored in /ai/data/ folder with versioning
Symbol metadata, order book snapshots, and logs are archived
---
7. AI Training Phases
---
8. Reward System (AI Evaluation)
Each AI model is rewarded based on:
Net Profit per trade
Slippage avoidance
Drawdown reduction
Spread/commission cost minimization
SL/TP efficiency
Proper use of hedging when volatility increases
---
9. Developer Notes
Execution Code (C++): Lives in C:\KIBotProjekt\fix_api_cpp
AI Code (Python): Lives in C:\KIBotProjekt\ai_core
Backtesting Data: C:\KIBotProjekt\data\backtests\
All code must be modular (each AI strategy is a class/module)
Logging and debugging enabled in both Python and C++ side
---
10. Optional Enhancements (Planned)
GPU acceleration with ONNX + TensorRT
Auto-strategy evolution with Genetic AI
Visual dashboard using Plotly + FastAPI
Execution fallback if FIX latency too high
Kafka for distributed AI agent deployment