01
Strategy library
Several strategies run side by side, each declared separately, so one can be switched off without touching the others.
AlgoReal-time · Python
Algorithmic systems
Strategies that are
measured, not guessed.
Automated trading systems running a library of strategies, signal-driven entries, backtesting and risk controls baked in. Built to execute reliably in real time and to be measured against historical data before a single rupee is committed.
Execution is the easy half
A strategy that cannot be replayed against history is an opinion. Every strategy in the library is backtested on the same data path it will trade on, and every position runs inside limits that are set before the market opens, not during it.
Multi
Strategies
24/7
Execution
Risk
Controls
Signal generation
Entry and exit rules
Position sizing and limits
Backtest against history
Live execution and logging
What it does
A library, and the discipline around it.
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Signal-driven entries
Entries and exits follow computed signals rather than discretion, which is what makes the results comparable month to month.
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Backtesting
Every strategy is replayed against historical data through the same code that trades it, so the test measures the system and not a copy of it.
04
Risk controls
Position sizing, exposure caps and stop conditions are part of the strategy definition, not a manual step after the fact.
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Real-time execution
The systems run unattended through the session, with each decision and order written down for review afterwards.
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Measured, not claimed
Performance is reported from recorded fills, so what the system says it did is what the broker statement shows.
How it is built
Python, end to end.
Python
Real-time Data Feeds
Backtesting Engine
PostgreSQL
Redis
Docker
Let's build
Have a strategy worth automating?
Tell us about the hard problem. We'll show you how we'd architect, build and ship it.