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Built a complete end-to-end automated trading system that captures alerts from TradingView, processes them through a Python-based webhook engine, validates multi-condition logic, and executes trades directly via the Tradovate API — eliminating manual intervention and improving execution speed and accuracy.
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Pepperstone is one of the most popular
forex brokers in the UK, UAE and Australia.
500,000+ active traders.
Most of them are still manually clicking
Buy and Sell.
Here's the problem with manual trading
on Pepperstone:
❌ You miss entries because you blinked
❌ You move your stop loss emotionally
❌ You skip trades because "it doesn't feel right"
❌ You can't trade London open at 8AM GMT
AND Sydney close at the same time
❌ You revenge trade after a loss
Here's what automation fixes — permanently:
Pepperstone supports two automation methods:
METHOD 1 — MT5 Expert Advisor
→ Strategy coded directly in MQL5
→ Runs inside Pepperstone MT5 24/5
→ No external server needed
→ Executes every tick automatically
METHOD 2 — Python + MT5 Bridge
→ TradingView generates signal
→ Webhook fires to Python server
→ Python connects to Pepperstone via MT5 API
→ Order placed with full SL/TP in milliseconds
What I automate on Pepperstone:
📈 Forex Majors — EURUSD, GBPUSD, USDJPY
🥇 Gold — XAUUSD (most volatile, most profitable)
📊 Indices — UK100, US500, GER40
🛢️ Oil — USOIL, UKOIL
₿ Crypto CFDs — BTC, ETH
Real example from last week:
XAUUSD setup detected at 3:47 AM GMT.
EMA crossover confirmed.
RSI momentum aligned.
ADX trending strong.
Volume spike — institutional move.
Bot placed the trade.
Hit TP in 4 hours.
+180 pips.
I was asleep.
This is not luck.
This is a system.
Are you trading Pepperstone manually?
Tell me your strategy below 👇
I'll show you exactly how to automate it.
#Pepperstone #ForexTrading #XAUUSD
#TradingAutomation #AlgoTrading #MT5
#UKTrading #UAETrading #AustraliaTrading
#TradingBot #Python #FinTech #GoldTrading
exactly , manual trading is obsolete
Built a Python Log Analytics Engine that can process 1,000,000+ log entries efficiently using streaming file handling and O(n) aggregation.
Key features:
• Fast line-by-line processing
• Reduced memory usage by avoiding full-file loading
• Frequency analysis & filtering
• JSON export for structured reporting
• CLI-based workflow
I enjoy building practical Python tools that solve real problems through automation and clean logic.
#Python #Automation #CLI #DataEngineering #Backend
good work Bhawna !
FinSecure: A High-Performance API with Role-Based Access Control (RBAC)
Security is the backbone of any fintech application. For FinSecure, I architected a robust backend infrastructure using FastAPI and PostgreSQL that prioritizes both data integrity and system speed.
I implemented a strictly enforced Role-Based Access Control (RBAC) system and JWT authentication to ensure that sensitive financial data is only accessible to authorized users. Beyond security, I focused on performance engineering—optimizing database schemas to reduce query latency by 15%. This project demonstrates my ability to deliver 'production-ready' code that balances high-level security protocols with the speed required for a modern user experience.
Secure Authentication: Integrated OAuth2 and JWT for industry-standard user sessions.
Granular Permissions: Developed a scalable RBAC system to manage complex user hierarchies.
Performance Tuning: Refactored SQL queries to handle high-concurrency requests without lag.
Clean Documentation: Fully documented API endpoints using Swagger/OpenAPI for easy frontend integration.
#FastAPI #Python #BackendEngineer #PostgreSQL #API
#FinTech #CyberSecurity #RBAC #SystemDesign #ScalableSystems
#Independent #RemoteWork #SoftwareEngineering #CommissionFree #BhubaneswarTech
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