Seeking an experienced quantitative developer to build a custom, AI-powered Telegram signal parsing and trade execution system with backtesting capability.

Seeking an experienced quantitative developer to build a custom, AI-powered Telegram signal parsing and trade execution system.

The goal is to automatically copy trading signals from multiple Telegram providers to MetaTrader 5 with ultra-low latency and intelligent signal interpretation.

Body: Looking for an experienced developer to build a custom signal copier system.
 
Skills Required:

- MQL5 / MetaTrader 5

-Telegram API integration

- LLM/AI Integration (Gemini Flash)

- Trading Systems

- Backtesting
- Python for AI-powered signal parsing
-Financial systems experience - Forex, Gold Trading experience

 I have a detailed RFP available.
Please DM with: - Relevant portfolio/GitHub - Approximate quote - Timeline estimate

 Key Technical Requirements Telegram Integration Connect via user's personal Telegram account (NOT bot-based)
Rationale: Avoids provider restrictions on bots; works universally
Must support reading full channel history for backtesting Signal Processing LLM-based parsing (Gemini Flash recommended for speed) Target processing time: <1 second per signal
Must handle varied signal formats without code changes AI training interface for adding new providers Latency Targets Telegram network: ~1s (unavoidable) Signal parsing: <1s Trade execution: ~0.3-1s Total end-to-end: <3s (excluding provider input time)
 Scalability Support up to 100 providers without performance degradation
Architecture must allow for this from

Phase 1 Core Problems to Solve Missed Entries: Current system misses fast-moving entries despite low broker latency
Telegram Bottleneck: Combined Telegram network + parsing latency causes delays
Limited Customisation: Need more control over signal interpretation and execution
Provider Validation: Difficult to verify provider profitability before subscribing
Complex Signals: Need intelligent parsing of layered entries, partial closes, and dynamic risk management