Three strategies have been coded to the best of my ability. + Lightning Fast Threading #709

Trendline: Buy when trend exceeds > 1 sell when trend is < 1 (down) where 1 is no trend at all. Standard Deviation: Buys when the mean of the last short trades exceeds the standard deviation and mean of the last long trades. Neural: Uses neural regression learning to predict the next price. Can be resource intensive. Be careful. Based on weather prediction sliding max/minimum. Buys when the mean of the last two periods prediction price exceeds the mean of the last 3 periods actual prices. I call it sliding 2/3rds averaging prediciton signal. Also: I attempted a fix for zenbot's single-thread node limitation by incuding cluster npm module, the whole zenbot instance is clustered from the master instance for neural and other processor-resource intensive strategy simulation. Be careful, it can be prone to a memory leak with: cluster.setMaxListeners(0). The cluster worker processes, if killed, should exit with: cluster.on('exit', function(worker, code, signal) { console.log('worker ' + worker.process.pid + ' died'); ----------------------------------------------------- The entire code for clustering: ----------------------------------------------------- var cluster = require('cluster'); cluster.setMaxListeners(0) var numCPUs = require('os').cpus().length; if (cluster.isMaster) { // Fork workers. for (var i = 0; i < numCPUs; i++) { cluster.fork(); } cluster.on('exit', function(worker, code, signal) { console.log('worker ' + worker.process.pid + ' died'); }); } else { ////////////////////////zenbot.js code/////////////////////// } -----------------------------------------------------

Threading code did not work correctly at all. Thinking of solutions. Not sure where to go with this. I need actual professional node advice. ``` if (s.lookback[s.options.min_periods]) { for (let i = 0; i < s.options.min_periods; i++) { tll.push(s.lookback[i].close) } for (let i = 0; i < s.options.min_predict; i++) { tlp.push(s.lookback[i].close) } s.my_data = tll.reverse() var learn = function (s) { s.done = 0 for(var j = 0; j < s.trains; j++) { if (cluster.isMaster) { cluster.fork(); } else { for (var i = 0; i < s.my_data.length - s.neural.neuralDepth; i++); var data = s.my_data.slice(i, i + s.neural.neuralDepth); var real_value = [s.my_data[i + s.neural.neuralDepth]]; var x = new convnetjs.Vol(data); s.neural.trainer.train(x, real_value); var predicted_values = s.neural.net.forward(x); if (i === s.my_data.length - s.neural.neuralDepth) { s.done = j } if (i === s.my_data.length - s.neural.neuralDepth) { calculating === 'False' } } } } } var predict = function(data){ x = new convnetjs.Vol(data); predicted_value = s.neural.net.forward(x); return predicted_value.w[0]; } if (s.lookback[s.options.min_periods] && calculating === 'False') { calculating = 'True' learn(s); } if (s.done === s.trains) { var item = tlp.reverse(); s.prediction = predict(item) s.mean = math.mean(tll[0], tll[1], tll[2]) s.meanp = math.mean(s.prediction, oldmean) s.sig0 = s.meanp > s.mean oldmean = s.prediction } ``` The above is why. How can I drop that into a new clustered function?

Had to roll back to node 7.x. But added napa/napajs microsoft threading npm module to package.json for future use. Updated package-lock.json Install goes smoothly. Included install log in install.sh for diagnostics.

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Three strategies have been coded to the best of my ability. + Lightning Fast Threading #709

Three strategies have been coded to the best of my ability. + Lightning Fast Threading #709

Commits on Nov 15, 2017