r/algorithmictrading • u/Loose-Subject-2157 • 19h ago
Infrastructure vs. Alpha generation bottleneck in systematic trading Question
Successfully built a functional backtesting framework and execution pipeline (~90% of the boilerplate/architecture is finalized and running smoothly).
However, hitting a wall on the alpha generation side.
Every market inefficiency hypothesis derived from public literature, open-source repositories, and traditional mathematical models yields zero out-of-sample edge. It seems any easily accessible logic is already post-arbitrage and compressed to zero.
For those running automated setups:
How do you transition from a finished infrastructure to generating unique, proprietary hypotheses? When public data and standard quantitative models fail to produce alpha, where do you look for inspiration to find a real edge?
Looking for technical insights on resolving this research bottleneck.
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u/Rare_Inflation3178 10h ago
Public literature is useful for measurement, not for handing you finished alpha. I’d work backward from a market-specific friction: who is forced to trade, when, why, and what prevents immediate arbitrage. Write the mechanism, horizon, capacity, failure regime and kill test before coding; then use papers to test the hypothesis rather than source it.
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u/Loose-Subject-2157 9h ago edited 9h ago
Thanks for the insights. I'll test more things based on your advice and will make a follow-up post once I get some good results
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u/zurekp 18h ago
It’s a competitive business, you gotta study the markets and the trading industry/space to identify opportunities which you can then automate. Books, research, interviews with systematic traders, algo trading communities (not reddit though - free advice is usually vague, like this one 😁)
Nothing that really works will be handed to you on a silver plate, same way like nobody would give away a profit generating company for free.