December 2014 – Consulting, Machine Learning, Predictive Engines

2014-12-122017-01-10

LuaJIT Access 20 Gig or More of Memory

Access 20 Gig or more from LuaJIT while coding in native Lua and minimizing GC speed penalties.

I started using LuaJIT© after first using F#, Python, Julia and C for stock and Forex related predictive work. I am always on the lookout for a language that is high speed as close as I can get to C without having to write in low level C all the time.

Lua is a language that feels somewhat like a cross between BASIC and Ruby and has been around for a long time. Lua may embedded or used stand-alone. It has been embedded into many games, entertainment consoles and other devices as a scripting language. The LuaJIT is a new compiler technology and takes what was already fast as an interpreted language and in some of our tests made it run over 20X faster with a few tests reaching 80X faster.

LuaJIT seemed like the ideal combination since it provided a language any ruby or python programmer would find readable with fast start-up times, excellent run-time speeds and good error messages. Continue reading “LuaJIT Access 20 Gig or More of Memory”

2014-12-102017-01-10

Sourcecode to save Forex Bar data to CSV via cAlgo bot

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. .

http://ctdn.com/algos/cbots/show/599

2014-12-012015-02-22

Lua jit tests faster than Julia for Stock Prediction Engine

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.

Performance comparison Julia versus Lua Jit

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.

Only 1 tested Julia operation was faster than Lua JIT

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”