BTCUSDT spot market SMA Trading Strategy on QuantConnect Platform -- 2
Budget: $250 – $750 USD
I need a fully coded, back-tested trading algorithm for the BTCUSDT spot market that relies on a Simple Moving Average (SMA) or another related indicators. All work must be done in QuantConnect’s LEAN framework, pulling historical and live data through the Binance API.
Here is the flow I expect:
• Build the SMA-crossover logic exactly as described, keeping parameters clearly exposed so I can tweak them later.
• Run a complete backtest from 1 Jan 2017 to today. The results must show at least a 60 % win rate, a 50 % compound annual growth rate, and no more than 20 % drawdown. If the first parameter set misses the mark, iterate until the goals are met.
• Once results are validated, enable QuantConnect’s live Paper Trade mode.
The strategy should:
– Push every trade signal to a private Telegram channel via bot API.
– Place the corresponding spot order on Binance through QuantConnect’s built-in brokerage integration.
- Hand over the fully commented source code, research notebook (if used), and a brief README explaining deployment steps inside a QC project.
I will consider the job complete when I can:
1. Open the QC project, run the backtest, and reproduce the required metrics.
2. Switch to live paper mode and see real-time signals arriving on Telegram while orders hit the Binance paper account automatically.
Python is preferred, though C# is acceptable if you document setup thoroughly. Please keep external dependencies light—anything beyond the standard LEAN libraries and python-telegram-bot (or equivalent) should be justified.
Let me know your estimated timeline to hit the targets and whether you need any additional API keys or channel tokens from my side.
Here is the flow I expect:
• Build the SMA-crossover logic exactly as described, keeping parameters clearly exposed so I can tweak them later.
• Run a complete backtest from 1 Jan 2017 to today. The results must show at least a 60 % win rate, a 50 % compound annual growth rate, and no more than 20 % drawdown. If the first parameter set misses the mark, iterate until the goals are met.
• Once results are validated, enable QuantConnect’s live Paper Trade mode.
The strategy should:
– Push every trade signal to a private Telegram channel via bot API.
– Place the corresponding spot order on Binance through QuantConnect’s built-in brokerage integration.
- Hand over the fully commented source code, research notebook (if used), and a brief README explaining deployment steps inside a QC project.
I will consider the job complete when I can:
1. Open the QC project, run the backtest, and reproduce the required metrics.
2. Switch to live paper mode and see real-time signals arriving on Telegram while orders hit the Binance paper account automatically.
Python is preferred, though C# is acceptable if you document setup thoroughly. Please keep external dependencies light—anything beyond the standard LEAN libraries and python-telegram-bot (or equivalent) should be justified.
Let me know your estimated timeline to hit the targets and whether you need any additional API keys or channel tokens from my side.