AI Strategies - TrendSpider

Introduction

Artificial Intelligence (AI) is a vague concept. In our case, "AI Strategy" is an umbrella term we use to name a set of underlying tools (like machine learning, LLM-based feature engineering, model crossbreeding) which work together so traders could benefit from advances in the AI/ML domain. On ...
How AI strategies are different from algorithmic strategies

Trading strategies originate from some kind of the past experience accompanied with ideas for the future. Let's think of the challenge of building a strategy in the following terms Data. There's a list of historical candles. For each candle, we already know how the price has changed after. Label...
Workflow for experimenting with AI models

There are many ways to go with the AI strategies. If you have particular ideas, then you definitely should start from them. If you’ve got hands-on experience with training AI models, then you might want to jump straight to the point, too. However, we have found that framing yourself into followi...
Creating an AI strategy: 1. Learning data set

Models need to be trained on historical data. You should define a market (or a set of markets) for your model to train at. “Market” is a combination of a symbol, chart time frame (like D) and time range (like from date A to date B). You can use any symbols TrendSpider supports, including your own...
Creating an AI strategy: 2. What you want to predict

Remember you’re building a fixed RR strategy. The AI Model will aim to provide a signal when at any time over the course of the next X candles the price will be likely to hit your TP and not your SL. Here’s a picture Remember that the actual trained model won’t catch all the occasions like tha...
Creating an AI strategy: 3. Type of the model

The world of machine learning is not new, and there are numerous machine learning model types out there. Different models work in a different way. There are a few heuristics which can help you to select the model type for the case. However, a typical approach is beautiful and simple: for every ta...
Creating an AI strategy: 4. Model inputs (features)

Defining your model inputs is a make-it-or-break-it kind of a thing. With awesome inputs (that is, "inputs legitimately having predictive power for the signal"), even the simplest model will learn well and work well. With poor inputs (that is, "inputs having nothing to do with the actual signal")...
Creating an AI strategy: 5. Assessing quality of models

Once the model is trained, you have some data to analyze. Here are 3 things you’ll have to work with. Analyze “Confidence vs Win%” chart. Explore input parameters' importance. Backtest your model on the data it has never seen before. In general, first hing you want to do is to discard bad models...
Creating an AI strategy: 6. Crossbreed good models

Crossbreeding models means picking a number of models and then generating a few new ones, which have all the parameters being similar to the parent models, but which have their inputs mixed in a few ways. When you cross-breed models, the system does the following: Pick a few “best” inputs, a fe...
Formula language reference

Formulas are expressions which operate with variables. Variables can be any of the following: ID of any of the other inputs you have provided for the model, whatever these inputs are. One of built-in variables which contain OHLCV values. Variables are: O, H, L, C, V. Formulas can have the follow...