u/AdMedical7654

A lot of people pointed out that the biggest issue is not just latency, but fill assumptions, queue position, partial fills, slippage, and transaction costs.

For people running short-term or high-frequency futures strategies, what do you currently use to test whether a strategy will survive live execution?

Do you use:

• NinjaTrader Strategy Analyzer
• Market Replay
• QuantConnect
• Sierra Chart
• Bookmap
• TT simulation/backtesting
• custom Python/C++ backtester
• broker/live fill logs
• spreadsheets/manual analysis
• something else

And what do you think is still missing from those tools?

For example, would it be useful to test the same strategy under different execution assumptions like:

• tick data vs Level 1 vs Level 2/order book data
• fixed latency settings
• different platform assumptions, like retail broker, VPS, TT, or co-location
• realistic fees and commissions
• limit order queue position
• partial fills
• adverse fills in fast markets
• market order vs limit order execution

I’m trying to understand how serious futures algo traders realistically validate fast strategies before going live.

What would make you actually trust a backtest for a short-term/high-frequency strategy?

Thanks for the replies on my last post. A lot of people pointed out that the biggest issue is not just latency, but fill assumptions, queue position, partial fills, slippage, and transaction costs.

For people running short-term or high-frequency futures strategies, what do you currently use to test whether a strategy will survive live execution?

Do you use:

• NinjaTrader Strategy Analyzer
• Market Replay
• QuantConnect
• Sierra Chart
• Bookmap
• TT simulation/backtesting
• custom Python/C++ backtester
• broker/live fill logs
• spreadsheets/manual analysis
• something else

And what do you think is still missing from those tools?

For example, would it be useful to test the same strategy under different execution assumptions like:

• tick data vs Level 1 vs Level 2/order book data
• fixed latency settings
• different platform assumptions, like retail broker, VPS, TT, or co-location
• realistic fees and commissions
• limit order queue position
• partial fills
• adverse fills in fast markets
• market order vs limit order execution

I’m trying to understand how serious futures algo traders realistically validate fast strategies before going live.

What would make you actually trust a backtest for a short-term/high-frequency strategy?

I’m doing some research on futures algo trading and wanted to ask people who actually build or run automated strategies.

When a backtest looks profitable but fails live, what is usually the main reason?

Is it more because of:

• slippage
• latency
• fees/commissions
• bad fill assumptions
• queue position on limit orders
• using candle data instead of tick/order book data
• overfitting
• platform/execution differences, like NinjaTrader vs TT vs VPS/co-location

The idea I’m trying to understand is whether traders would find value in a tool that lets them test a strategy under different realistic execution assumptions before deploying it live.

For example, you could test the same futures strategy with:

• tick data vs candle data
• 1 ms latency vs 500 microseconds vs 50 microseconds
• 1 tick or 2 ticks of slippage
• realistic fees and commissions
• different platform assumptions, like retail broker/VPS/TT-style execution
• estimated queue-position effects for limit orders

The goal would not be to guarantee live results, but to see if a strategy still survives under more realistic execution conditions before spending money on better infrastructure.

For people who run futures algos, is this a real problem you deal with? What do you currently use to test this?

I’m doing some research on futures algo trading and wanted to ask people who actually build or run automated strategies.

When a backtest looks profitable but fails live, what is usually the main reason?

Is it more because of:

• slippage
• latency
• fees/commissions
• bad fill assumptions
• queue position on limit orders
• using candle data instead of tick/order book data
• overfitting
• platform/execution differences, like NinjaTrader vs Trading Technologies vs VPS/co-location

The idea I’m trying to understand is whether traders would find value in a tool that lets them test a strategy under different realistic execution assumptions before deploying it live.

For example, you could test the same futures strategy with:

• tick data vs candle data
• 1 ms latency vs 500 microseconds vs 50 microseconds
• 1 tick or 2 ticks of slippage
• realistic fees and commissions
• different platform assumptions, like retail broker/VPS/TT-style execution
• estimated queue-position effects for limit orders

The goal would not be to guarantee live results, but to see if a strategy still survives under more realistic execution conditions before spending money on better infrastructure.

For people who run futures algos, is this a real problem you deal with? What do you currently use to test this?

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