Consulting, Machine Learning, Predictive Engines
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When I learn new programming languages I first choose a non-trivial programming problem and then develop pieces of that system in the new language then go back and re-factor as I learn more. Solving a real problem with the language is the fastest way I have found to gain demonstrated competence in a new language. Continue reading “Scala Resource Links Getting started”
Lua Source demonstration of using Kelly Criteria to figure out appropriate amount to risk for any single trade based on your current capital, odds of winning and win versus loss payout. Continue reading “Lua Source to calculate Kelly Criteria Investment Level”
I recently started making Forex Micro-strategies available for sophisticated users under the general terms that they agree to pay 15% of their net profit. Those that pay will also gain the benefit of continued enhancements and new micro strategies. Contact us for access. Continue reading “Precision VS Recall, a Net Trading Profit Perspective”
I needed this utility to save 1 minute bar data tick data in a form where our AI / ML prediction engine could access it. I needed the history but even more important I needed the something that could deliver new ticks to the prediction engine fast as they became available.
I cheated with this utility and open the file in shared mode. This allows my Lua code read new lines as they become available as if the data was delivered by a more traditional pipe. I tested the lua code which reads to the end of the file and then recognizes when new data is available simply by continuing the read process. When no data is available it returns immediately so you can do a very low cost poll without having to re-open or re-read any part of the file. When I move to linux I can replace the file with a named pipe and eliminate the overhead of writing to disk.
Not a elegant architectural approach but it does keep the interface between my proprietary code and the 3rd party cAlgo product as thin as possible so I can change brokers easily in the future.
I was able to get it to deliver 1 minute bar data back to Jan-2011 which is the best source I have found for that much data. Still has a flaw in that I could not figure out how to calculate actual transaction volume for the bar but will comeback to that when it is critical. .
I started testing Julia as a possible alternative because Julia advocates claimed the interpreter loop was nearly as fast a C and it was similar in concept to Python which I love but which was too slow for our application. I recently ran across a blog entry mentioning a new Lua Jit. I found it intriguing because Lua did quite well during our last round of tests.
| Relative Execution Time. Lua Jit as baseline – lower is better | ||||
| Operation | Lua52 | LuaJit | Julia | Julia Typed Array |
| Parse File into Data Frame | 2.42 | 1.0 | 5.64 | 5.64 |
| Compute SMA(14) | 2.81 | 1.0 | 6.87 | 0.70 |
| Compute SMA(600) | 33.32 | 1.0 | 80.00 | 1.30 |
| Compute SMA_slice(14) | 2.42 | 1.0 | 11.87 | 1.83 |
| Compute SMA_slice(600) | 33.32 | 1.0 | 15.52 | 5.90 |
| Did not implement slice in Lua so re-used the timing from nested loop version. Response times are in seconds. |
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The only function where Julia out performed Lua Jit was in the SMA(14) all other items tested were slower. I think the reason it did better in this instance is that the SMA function must allocate a new array with 71K rows to store the results. In Julia you can do this as a typed Array of float. In Lua this is done as an append to list so it is allocating memory in little pieces. In the SMA(600) the Lua jit was faster again because it is doing more work compute in a tight loop relative to the memory allocation overhead.
Continue reading “Lua jit tests faster than Julia for Stock Prediction Engine”