(this is part 5 of the “AI for Finance Course“) Now we’re going to apply this knowledge for training our single neuron (perceptron) to the real market.(specifically, Bitcoin price) The most simple thing we can do is to use previous candle to predict the next candle. And we’re not going to complicate much with the exact price. We’re just going to try to predict whether […]
NEXT LESSON:Single Neuron Market Predictions
(this is part 3 of the “AI for Finance Course“) In previous lesson, we saw significant improvements when it comes to handling low input values (including zero). However this is by far the end of the complexity we could find in data. In fact, it’s the beginning. Just take a look at these inputs: double[][] inputs = [ [0.0, 0.0], // 1 [0.0, 1.0], // […]
(this is part 2 of the “AI for Finance Course“) Previously, you might have seen how the most basic form of a neuron simply cannot handle all the data that is thrown at it, specifically low values. This means, if both of our inputs are zero, regardless of how good our weights are tuned, the sum will always be zero.(and consequently, sigmoid function will always […]
(this is part 1 of the “AI for Finance Course“) Imitating nature is always a good way to make technical progress. Airplanes copied birds, submarines copied fish, and neural networks copied neurons. Or at least they were inspired by neurons. The left side represents sensory inputs where signals are received. And the right side represents the output where signal is emitted. Obviously, not every input […]
no complex math✅no derivatives & integrals✅elementary school math✅and plain old english✅ This is a series of blog posts covering neural network price predictions, everything from a single neuron to a more complex structure. The main idea is to go through entire process incrementally, and to build upon previous solutions. This allows you to never be lost and/or overwhelmed. This course is work-in-progress and I’ll be […]
The premise of this strategy is that whatever is happening will continue happening (especially when it comes to a strong trend). In this example, I’m using 5 consecutive green candles as an indicator of a strong trend. Because something is obviously causing such a clear move in one direction. entry position (white dot) and exit position (yellow square) In the code below, using the Badger […]
In random events, “streaks” have low probability, and this strategy is trying to take advantage of this fact by betting against the “streak”. Here we see 3 consecutive red candles before the algorithm enters the trade: entry position (white dot) and exit position (yellow square) In our code, using the Badger algo-trading framework, we can see that the actual candles taken into consideration are… …but […]
Moving-average crossover strategy assumes that we can detect the change in overall trend by catching it early on a shorter time frame. entry position (white dot) and exit position (yellow square) In the code, using the Badger algo-trading framework, we’re comparing 10 and 20-candle moving average (those are 1-minute candles). First we need to verify that the ma10 is below the ma20. Then once it […]
Similar to local minimum strategy, the idea is to wait for price to go somewhat to the extreme and then wait for pullback/retracement. The degree to which you want the price to go to extreme (before entering the trade) is completely up to you. In this code example using the Badger algo-trading framework, I set the percentile to 20% (i.e. when price was at the […]
This strategy is based on the observation that the price tends to reverse/retrace from extremes and this algorithm captures such behavior. When the price reaches local minimum (20 candles), the trade is executed, expecting the price correction in opposite direction. entry position (white dot) and exit position (yellow square) Below is the code that uses Badger algo-trading framework for backtesting and visualization: // LOCAL MINIMUM […]
The simplest code example for Badger algo-trading framework is “buy and hold” strategy where your main focus is picking a good entry point. This code example picks the entry point at random (at the beginning of the backtestPeriod). entry position (white dot) and exit position (yellow square) But you can decide based on variety of factors like: // you can also add custom variables here […]