Markets to make math & machine win it for you.
Analysis is Not Enough to Win – Amateur and professional traders, analysts, investors have spent years trying to develop the required skills and strategies that “might” yield a profit on their trades.
People have spent long hours in front of the screen, trying to figure out what was going on with the charts and indicators.
They have spent nights and days analysing patterns, price movements, reading the news and trying to catch the best moment to enter a trade.
The Technical Analysis approach has suggested determining support, resistance levels, drawing lines and utilizing technical indicators to figure out when to enter or exit a trade returns.
For many decades, the traders and investors have looked for ways to forecast the future prices of the stocks, commodities, funds as well as currency ratios.
Fundamental Analysis suggested the value investing approach must be used when picking the best assets to invest in.Both of these methods had some certain trade-offs and none have yielded a reliable and robust strategy that generated high returns.
The result is that, only 5% of all trades performed are generating profits, and the rest 95% are causing large losses.
Traders learn to develop the winning strategies the hard way, by losing vast amounts of time and money. And these losses still don’t help finding out the winning trading strategy.
Why humans need machines for math
Parameters affecting the asset prices cannot be understood and analysed by humans at once, as the human brain’s capacity has limitations. Human brain cannot take multiple inputs at the same time and analyse them numerically.
One cannot analyze the current level of RSI, MACD, Williams %R, Stock Momentum, Various Oscillators, market Sentiment and so on, at the same time on a numerical manner.
There are more input parameters affecting the future price movements of the assets, that human brain can receive, store, analyze and predict in a very short time interval.
You need a strong, computer backed model which collects, stores and analyzes the major components affecting the price of an asset. It is proven that, the Machine Learning and Deep Learning Models perform well on dealing with such complex tasks.
The Neural Network structure mimics the human learning process by optimizing the “weight” and “bias” values of an Artificial Neuron, as the information flows through it.
These structures also have memory states which can understand and extract the correlations between the current state and the past states of the data.
The Neural Network models learn the complex relationships between different kinds of financial data using performance optimization, error minimization and back propagation techniques.
After the network weights and biases are adjusted and the hyper parameters are optimized, the network validates and tests its performance on the unseen future data.
All these processes require massive computational power and experienced engineers to create and “train” the Neural Network Models.
These models then generate the future price predictions for the subject financial assets, way more precisely than any other known method.