Many #tradingview strategies look strong in backtests — but fail quickly once they go live.
The reason usually isn’t the strategy idea itself.
Real deployment introduces challenges that backtests often miss:
• Slippage
• Exchange execution delays
• API reliability
• Position sizing errors
• Risk controls
• Live monitoring
A profitable backtest does not automatically mean a strategy is ready for real capital.
This is where many traders underestimate the true complexity of automation.
A more realistic process often looks like:
#tradingview / #pinescript strategy
→ strategy conversion
→ backtest
→ paper trading
→ small live deployment
→ full automation
The biggest gap in automated trading is often not strategy creation.
It’s execution.
We’ve been closely focused on this strategy-to-execution gap at #VegaXArchitect , especially around simplifying how traders move from TradingView strategies to live deployment.
How are others here validating their strategies before going live?
The reason usually isn’t the strategy idea itself.
Real deployment introduces challenges that backtests often miss:
• Slippage
• Exchange execution delays
• API reliability
• Position sizing errors
• Risk controls
• Live monitoring
A profitable backtest does not automatically mean a strategy is ready for real capital.
This is where many traders underestimate the true complexity of automation.
A more realistic process often looks like:
#tradingview / #pinescript strategy
→ strategy conversion
→ backtest
→ paper trading
→ small live deployment
→ full automation
The biggest gap in automated trading is often not strategy creation.
It’s execution.
We’ve been closely focused on this strategy-to-execution gap at #VegaXArchitect , especially around simplifying how traders move from TradingView strategies to live deployment.
How are others here validating their strategies before going live?