How to Predict Stock & Crypto Prices with Machine Learning: Approach Model

It is no secret that the financial market can be a volatile place. What goes up, often comes down — and vice versa. This unpredictability is what has led many investors to seek out ways to predict stock and crypto prices using machine learning.

Predictive modelling allows you to analyze past data in order to better understand how a particular asset will perform in the future.

In this blog post, we will discuss how you can use machine learning algorithms to predict stock and crypto prices — and potentially make some significant profits!

Important Consideration

I’m?not going into specifics about who to develop a prediction tool in this blog. This is more about how to apply and approach machine learning as a method for predicting market prices of various assets, such as stocks and cryptocurrencies.

Price prediction is difficult to do with machine learning,?let alone manually,?because the data set that needs to be analyzed is often times very large and changes rapidly. In order to make an accurate prediction, it’s important for the machine learning algorithm to have access to a data set that is as up to date as possible. Additionally, the algorithm must be able to identify patterns in the data set that can be used to predict future prices. This is often easier said than done, given that the data set can be quite noisy and there can be a lot of variability from one day to the next. Additionally, stock prices are affected not just by economic factors but also by political and social factors, which makes it even more difficult to make accurate predictions.

With that in mind,?when individuals ask questions like “Will Bitcoin reach $100K by June 2022?” or “Is Cardona a good investment to make money in 6 months,”?they appear extremely ignorant because we don’t have the knowledge or capacity to anticipate and plan for every element and feature that drives the financial market.

So please, STOP THE MADNESS!?If anyone had the?actual ability to predict the future of finance with high accuracy, the world will be ruled under a single tyrannical rule of this individual. Use your common sense and stop asking questions that have no rational value.

Instead, try to learn and understand what makes the price of these assets move in the first place; what factors should I be looking at to make a calculated assessment?

We?can achieve?enhancement in getting closer to some sort of accuracy on price predictions of the financial market based on the scope but to be 100%, at this time, even with technology, is not feasible. Impossible, I will refrain from stating, however, as there are smarter people out there than myself who will one day make it a reality. I can’t wait!

Till then, learn to understand the process instead of just asking for a quick fix answer that is absent in rationale.

Prediction Analysis Types

There are two main types of analysis that investors use to predict stock and crypto prices: fundamental analysis and technical analysis.

1. Fundamental analysis?is the study of a company’s financial statement in order to determine its intrinsic value.?Key characteristics include:

• Evaluates a company’s stock by looking at its inherent value, which includes but is not limited to tangible assets, financial statements, management effectiveness, strategic goals and initiatives, and consumer behaviors.
• Is a long-term investment indicator that employs historical and present information to assess revenues, assets, expenditures, liabilities, and so on.
• Short-term news has little impact on the conclusions drawn from fundamental analysis.

2. Technical analysis, on the other hand, looks at historical price data in order to identify patterns and trends that can be used to predict future price movements.?Key characteristics include:

• Analyzes quantifiable data from stock market activities, such as stock prices, past returns, and trade volume. Quantitative information that might identify trading signals and capture the movement patterns of the stock market
• Focuses on historical and current data in the same way as fundamental analysis, however it is primarily used for short-term trading.
• Easily affected by news because they have a short time horizon.

Both approaches have their own strengths and weaknesses, but most investors believe that using a combination of both is the best way to achieve success in the financial market.

Looping in Machine Learning With Analysis

Both analysis techniques tie into machine learning in the following manner…

·?On the technical side,?machine learning can be used to identify patterns in stock price data that might indicate future movements. For example, a machine learning algorithm might spot a trend in how a particular stock tends to move after certain events occur. By understanding these patterns, the algorithm can then make predictions about where the stock price will go next.

·?On the fundamental side,?machine learning can be used to help analyze a company’s financial data to find trends and predict its future performance. For example, if a company’s reported earnings are consistently lower than analyst expectations, a machine learning algorithm could predict that the stock price is likely to go down.

Overall, machine learning can be a powerful tool for assessing both the technical and fundamental elements of financial price predictions optimally and efficiently.

What Is Machine Learning (ML)?

Machine learning is a method of teaching computers to learn from data, without being explicitly programmed. The application of machine learning has exploded in recent years, owing to the availability of large data sets and advances in computing power. One area where machine learning is beginning to be used extensively is in the stock & crypto market.

Tip: Most people use Machine Learning (ML) and Artificial Intelligence (AI) interchangeably, when in reality they are two different concepts. AI is a method of automating tasks that have no learning element to them and simply repeat the same actions over and over. Machine learning, on the other hand, teaches it self to improve in different ways as it is asked to perform a task, learning with the constant feed of data and features.

In a nutshell, AI provides repetition automation while ML develops human-like consciousness that learns and improves with experience.

Do you see why it is so difficult to master?!

Working With Algorithms

Machine learning algorithms can be used to predict stock & crypto prices in two ways:

1. By predicting the direction of the asset price movement?— this is done by analyzing past data to identify patterns that can be used to make predictions about future prices. For example, a machine learning algorithm might be taught to recognize that stocks that have been rising in price are more likely to continue rising than stocks that have been falling in price.
2. By predicting the magnitude of the price movement?— used to develop models of how different factors (such as news announcements, economic indicators, and company performance) affect security prices. These models can then be used to generate trading strategies that take advantage of predicted price movements.

There are a number of pros and cons to using machine learning in the financial market.

Benefits

• It can handle large amounts of data more efficiently than humans.
• It is not biased by personal opinions or emotions.
• It can detect patterns that humans might not be able to see.
• It can make predictions in real time, as new data becomes available.

Disadvantages

• Subject and limited to the skill set of the architect / engineer
• Subject to the bias of the architect / engineer i.e. are they considering elements inclusively or excluding based on personal experience or lack of knowledge
• Can produce inaccurate predictions due to type of data (limited, structured, unstructured, lack of variables, fake news etc.)
• Limited to variables that are identified during the initial modeling and if the economics change, without manual interference, it will provide inaccurate results

In the end, machine learning aids us in getting closer to a certain degree of accuracy and quantitative precision in predicting financial market pricing. However, before we go through the procedure and execute it, we must consider potential downsides and adjust our tactic in accordance.

How Does ML Work With Price Predictions?

So, how does one go about predicting stock and crypto prices using machine learning? Generally speaking, there are two main ways:

• Supervised learning -This approach requires you to have a dataset of historical data (e.g., stock price movements over time). You will then use this data to train a predictive model. Once the model is trained, you can then use it to predict future stock prices.
• Unsupervised Learning —?This approach does not require a dataset of historical data. Instead, it relies on other features (e.g., company news, economic indicators, etc.) to predict stock prices.

Importance Of Data Structure

The first question in the preceding section,?“what kind of data do you have?” is crucial when it comes to building machine learning models because it requires the data to be classified into two categories:

• Structured Data?— is easier to process and analyze than unstructured data. It’s usually arranged in a fixed format that makes it easy to extract specific pieces of information, which can be helpful for certain types of predictions. For example, if you’re trying to predict how if the price of stock will go up in the next month, you might find it helpful to use data that’s been formatted as a table or spreadsheet. This type of data works best with supervised learning models.
• Unstructured Data —?can be a valuable source of information for predictions in machine learning, because it can contain more diverse and nuanced information than structured data. For example, unstructured text data can include information about the sentiment or emotional state of a customer, which might be useful for predicting whether that customer is likely to churn. This type of data works best with unsupervised learning models.

Which Approach Is Better?

There are a few factors to consider when trying to predict stock and crypto prices using machine learning techniques. Questions to include as part of the analysis include:

High-Level Machine Learning Process Flow

1. What is the nature of the problem you are trying to solve??Involves factors and features that help defines your overall scope with its various use case scenarios. For example, a problem would be predicting price of stock in an overall industry sector, let’s say technology, with criteria’s that slice it further for additional scenarios which can include, sector to be software, timeframe to be 6 months, list of companies that are only in emerging markets with an annual revenue of $100M or more.
2. What kind of data do you have??If you have strong historical data with many features and structured data sets, then supervised learning could be a good option. This is because you can build a model that captures the relationships between the different features and the stock price. However, if your data is more limited and unstructured, then unsupervised learning might be a better option. This is because you can use clustering techniques to group similar stocks together and then make predictions based on those groups.
3. What kind of accuracy do you need??If you only need relatively accurate predictions, then unsupervised learning might be sufficient. However, if you need highly accurate predictions, then supervised learning will likely be necessary.
4. Does your accuracy impact your timeframe of prediction??Time has a significant influence on how data is used in machine learning. For example, the number of variables and features that go into your model may vary if you are seeking for a price prediction in a week vs 6 months. Shorter timeframes are better for time-based linear calculations (historical pricing) while longer periods of time perform better with nonlinear calculations that require additional data points to calculate an accurate forecast (unsupervised learning will work better).
5. How noisy is the data??Is structured and labeled or is it unstructured and has undefined factors that drive the price like news, politics, regulation & policies etc. If you don’t have enough labeled data or the cost of labeling them is prohibitive, unsupervised will perform better than supervised. In general terms, if Stock Price prediction is limited to a binary class (Buy/Hold/Sell), then unsupervised may do somewhat better owing to its ability to handle complex feature relationships (dependencies between features & factors), whereas in supervised learning most models will require feature engineering to achieve the similar results.
6. What features should we include for predictions??The features that you use will have a direct impact on the accuracy of your predictions. Key factors to consider for all types of assets inclusive of stocks, crypto, ETFs, mutual funds, bonds etc. are:

·?Company’s Financial Statement

· Annual Net Profit

· Net Profit Growth % YoY

· Market Cap (subjective)

· Debt (what are they accruing debt for specifically)

· Research & Development (Innovative or to keep up with competition)

· Management Changes & Presence (what are they doing for company to become great or are there significant changes expected in management)

· News (fake and real)

· Market Sentiment (how consumers feel about the product / services)

· Competitive ranking (how it is performing against the market)

· Industry

· Sector

· Geography (location, region)

· Emerging Market (Yes, No)

· Type of products / services offered

· Market Condition (Bull or Bearish)

· Others (as you see fit your problem criteria)

Tip:?Each of these features will provide valuable information that can be used to make predictions. However, it is important to note that the accuracy of your predictions will ultimately depend on how well you can engineer these features.

Factors Not To Consider With ML

In addition, there are a number of factors that?you should not consider?as the core basis of your approach when using ML on financial market predictions. They include:

·?Should not put too much weight on past performance.?Just because a company has been doing well in the past doesn’t mean they will continue to do so in the future.

·?Should not only focus on the stocks of large companies.?While they may be more stable, small-cap stocks often have more room for growth.

·?Should not blindly follow the crowd.?Remember that everyone is trying to predict the future and no one has perfect information.

·?Don’t get attached to any one stock/asset type.?The goal is to make money, not to find your new best friend. Diversify and stay on top of what is hot, trending and profitable.

Suggested Computational Investing Algorithms To Use For Price Predictions

Once we’ve decided on an approach, we should start considering what have most PHD’s, mathematicians, and economists turned to for their models (don’t reinvent the wheel here, use and improve on its basis).

There are a number of different machine learning algorithms that can be used for stock price prediction.?Some of the most commonly used algorithms include:

Linear Regression — used and works best when the data is linearly separable (i.e., clear boundary between the two classes). This means that there is a clear relationship between the dependent and independent variables. If your data is not linear, then a linear model may not be the best choice. This approach is just trying to predict the direction of price movement (up/down) and does not do well if the data is too noisy and unstructured

Logistic Regression — used to model binary outcomes (e.g. yes/no, success/failure, alive/dead). In the context of stock price prediction, it can be used to model whether or not a stock price will go up or down.

Random Forest — This is a type of ensemble learning algorithm that is made up of a number of decision trees. It is used to predict the probability of an event occurring, such as whether a stock will be sold or not. Random forests are particularly well-suited for problems where the predictor variables are highly correlated or where the data is relatively noisy. In these cases, using a single decision tree may lead to overfitting (i.e., the model becomes too specific to the training data and does not generalize well to new data). The random forest algorithm avoids this issue by building multiple decision trees, each of which is trained on a randomly selected subset of the predictor variables. This helps to “breed” more accurate models from less-accurate ones. Random forest typically performs better than logistic regression. This is because random forest can handle nonlinear relationships better than logistic regression, and it is also more resistant to overfitting. Overfitting occurs when a model becomes too specific to the data that it is trained on, and as a result, its predictions are no longer accurate when applied to new data.

Support Vector Machines (SVM)- can help find patterns in data and then use those patterns to make predictions. In stock market prediction, SVMs can be used to look for patterns in past prices and then predict future prices based on those patterns. This approach works best with data that is clear and well-labeled. If the data is noisy or has lots of missing information, this model may have trouble finding meaningful patterns.

Neural Networks — are better suited for tasks that require more complex predictions and are used to learn the hidden relationships between data points (helps you find factors through data). For example, LSTM (Long Short-Term Memory) models are a type of recurrent neural network that are particularly well-suited for predicting long sequences of data, such as stock prices. LSTMs can “remember” information for extended periods of time, which allows them to better predict how a particular stock price will change over time.

Gradient Boosting Machines (GBM) — works by fitting a series of weak learners to the data and then combining their predictions to form a strong final prediction. This model can handle large amounts of data and complex models, which is important in the stock market where there is a lot of information to process. In addition, GBMs are highly accurate; this is crucial when predicting stock prices and providing information quickly, which means that they can generate predictions quickly and easily.

No matter which approach you choose, there are a few things that you should keep in mind when trying to predict stock prices using machine learning.

Firstly,?always test your model on out-of-sample data?(i.e., data that was not used to train the model). This will help you to avoid overfitting, which is when a model performs well on training data but not so well on new data.

Secondly,?don’t put all your eggs in one basket.?Diversify your portfolio by investing in different stocks (or other assets) and don’t put all your money into one stock. This will help to protect you from big losses if the stock price falls.

Thirdly,?don’t forget about fees.?Most machine learning models require some sort of computational power, which can be expensive. Make sure you factor in the cost of using a machine learning model when making your predictions.

Finally,?always be prepared to learn.?The stock market is a complex system and it can take time to learn how to predict stock prices using machine learning. Be patient and keep practicing!

Considerations for Testing & Refinement

Proper and through testing is a critical element in driving the accuracy of machine learning models. As such, there are a few key considerations that need to be taken into account in order to ensure an accurate and reliable result. Here are a few of the most important things to keep in mind:

1. The size of data —?the more the data the better your results and accuracy as the model is accounting for greater variables and patterns to quantify the accuracy of the results.
2. The quality of data —?Not all data is created equal. In order for your machine learning models to perform well, you need high-quality data that is representative of the problem you are trying to solve. This means that the data should be clean of noise and errors, represent the problem we are trying to solve fully and is variable enough to account for all use cases.
3. Normalization of data?— This can be done by scaling the values to a range of 0–100, or by using standard deviation to bring all values within a similar range. This will help to remove any bias that may be introduced due to the natural variability in the data.
4. Splitting of data into training / test sets?— important so that you can train your models on one set of data and then test their accuracy on another set.
5. Evaluation of data —?You’ll need to evaluate multiple metrics, such as precision and recall. This will give you a better idea of how well your model is performing.
6. Validation of data?— To prove confirmation of the ML models in action have quantitative results that either prove that the model employed by the ML algorithms are effective, need modification, or require a complete paradigm shift. This step is the “tuning” aspect of the testing approach
7. Training the model with data?— Once all 6 steps are in rhythm, the training of the ML model begins automatically as more data is fed over time, the results should reflect closer accuracy to solving the original problem statement with factors of impact than what was initially started
8. Optimization and retraining of data?— You need to optimize and retrain data when your model is up and running and performing well. There will be periods when modifications are required due to a variety of reasons, such as an increase in features or criteria, new technologies, and / or problems with performance. As this occurs, an update to the current ML logic is required with the necessary information that ultimately retrains the model with the new data set and is a cycle to be re-performed till its at a stable result (repeat step 1 to 7).

Proper testing is essential in order for your machine learning models to be accurate and reliable. By following these key steps, you can ensure that your models are tuned for success.

Suggested Prediction Tools & Sites

Ready to start using Machine Learning as a tool to predict market prices BUT do not have the technical expertise or time to build a model? Here are some recommendations on sites that do the dirty work for you. Please note that I have included free and paid options and strongly suggest investing in the paid suggestion as anything free will give you a high degree of inaccuracy (we already established this is hard so why would it be free ??).

Conclusion

Predicting stock prices using machine learning can be a great way to make some significant profits. However, it is important to choose the right approach and to keep in mind a few key points. If you do this, you will be well on your way to becoming a successful investor!

Key Takeaways

Listed below are steps you should take when looking to approach financial market price predictions with Machine Learning (ML).

1.?Understand the importance and application of fundamental and technical analysis of an asset?(such as stock & crypto)

2.?Utilize the analysis to create your problem statement for solutioning?with machine learning

3.?Understand the differences between the two different types of machine learning approaches as it pertains to various scenarios;?supervised and unsupervised

4.?Understand the importance of data?you are working with as it directly impacts your approach, solutioning and limitations

5.?Apply the approach (or combination of both), that best fits your problem statement by considering the following questions:

·???????§ What kind of data do you have?

?·???????§ Large /big, incomplete, unstructured, structured, streaming, static etc.

?·???????§ What kind of accuracy do you need?

?·???????§ % based closeness that is consistently displayed with the features and factors involved

?·???????§ Does your accuracy impact your timeframe of prediction?

?·???????§ Does the timeframe impact your use case i.e. provides different results and impacts different factors as you lengthen or shorten the timeframe?

?·???????§ How noisy is the data?

?·???????§ Messy, incomplete, big, unstructured, fake news, corrupt etc.

?·???????§ What features should we include for predictions?

?·???????§ Factors & criteria that will help streamline your approach such as timeframe, historical price, net profit YoY, industry etc.

6.?Plan for testing tactic in the planning phase;?as everything is data driven in ML, understanding how your dataset impacts your result can only be anticipated by identifying potential areas of weakness or error, testers can focus their efforts on problematic or unclear areas ahead of build and increase the chances of finding issues before they cause problems.

Do you have any experience with predictive modelling? I would love to hear about it in the comments below!

Thanks for reading.

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Disclaimer

I cannot express strongly enough that you should use caution with the information supplied. I am not a professional financial adviser nor a fortune teller than can predict your circumstances or future. The data provided is meant to help individuals learn and adapt it for their own use, however it won’t always apply in every use case. I can confirm that these strategies have been extremely profitable in my personal life, contributing to my professional success.