User Guide

7. Trade Strategies Guide

Strategy Details

Crypto-ML’s trading system leverages multiple machine learning trading strategies to deliver trades to our Trader and Auto Trader customers. In this guide, you’ll find information on how each trade strategy functions.

Overview of Trade Strategies

Crypto-ML currently offers four trading and investment strategies:

HoldKeeps a designated percentage of your portfolio held in cryptocurrency.
CML-AAnomaly Detection: Measures the predictions from our neural networks to find anomalies in the market that indicate market manipulation that should be followed.
CML-I Investment: This strategy utilizes the CML-A machine learning foundation, but targets longer-term positions rather than swing trades.
CML-T Technical: Applies deep learning to technical analysis data in order to generate a high-probability rules-based system.
CML-XExperimental: Leading-edge strategies still in the research-and-development phase.

Except for Hold, these strategies all seek to compound your money over time by capturing numerous wins. No single strategy works in all conditions. That is why it is advantageous to run multiple strategies.

CML-A: Anomaly Detection

CML-A trading strategy issues trades by finding anomalies in our neural network price predictions. This ideally allows us to identify and trade with unexpected events, market manipulation, and other exceptional scenarios.

Our machine learning seeks to predict market movements based on past patterns. In other words, it may say “when the market looks like this, 96% of the time, price moves up.”

The reality is machine learning will likely never predict 100% of market movements. There is always going to be a certain amount of chaos. And there will always be a certain amount of anomalous behavior, whether it be market manipulation, unexpected news, or some other exceptional event.

If one of our neural networks predictions are right 94% of the time, but we know that other 6% is highly interesting, then the CML-A strategy is our solution.

CML-A exploits the known limitations of machine learning and statistics to identify unusual price movements as they are unfolding.

The name “Anomaly Detection” comes from an understanding of the way it operates. Here are some examples:

  • When the market patterns look like they do now, 97% of the time, price goes up by a small amount. However, now it is going up by a large amount. Current behavior does not match past patterns. This is a bullish anomaly. Something unusual is causing the market to go up.
  • Likewise, if the market normally would go down but instead goes up, this is a bullish anomaly as well. Instead of pulling back like normal, some unusual force is pushing price up.
  • These situations can also be viewed through a bearish anomaly lens. When the market patterns look normally result in a sideways price movement, but instead price is dropping quickly, there must be something causing this unusual movement.

As you can see in these examples, the market is behaving abnormally. It is likely being manipulated by some unusual activity or force. It is this force we want to identify and follow.


Strengths of CML-A

Whereas machine learning predictions may miss out on strong rallies and poorly handle strong drops (because these are exceptional conditions), CML-A will better capture rallies and better avoid drops.

CML-A has the ability to generate large gains and constantly trade through major upswings.

Weaknesses of CML-A

Under normal, non-extreme market conditions, CML-A may deliver choppy trades that result in small losses.

Additionally, all profit target and stop-loss conditions are handled internally, therefore the values cannot be displayed on the Member Dashboard or in the WebSocket API.

► Read the complete CML-A Overview

CML-I: Investing Machine Learning

Whereas our other strategies fall into a “swing trade” category, CML-I targets longer-term positions, capturing bigger market movements.

CML-I is based on the same technology foundation as CML-A. That means it utilizes the same prediction, anomaly detection, and trade mechanics engines. As such, CML-I is really just a tweak to the existing platform.

CML-I also gathers the same data as our Market Index. With this, it can help cryptocurrency investors navigate bull and bear cycles.

Put together, it’s considering the following inputs:

  • Exchange data
  • Technical indicator data
  • Social sentiment and volume data
  • Search sentiment and volume data
  • Bitcoin dominance data

Learn more about this approach in our Machine Learning Technology Guide.

In practice, CML-I generates about 25% the quantity of trades that CML-A does.

Strengths of CML-I

  • CML-I utilizes a longer-term perspective in its decision-making, helping to avoid the choppiness of intra-day cryptocurrency markets. CML-I is designed to seek out and capture longer bull runs.
  • While holding is similar to longer-term investing, the reality is markets move in cycles and do not go up forever.
  • Moving some of your funds out as the market becomes over-extended and then buying back in during the lull of a bear market can result in significantly improved returns. If you are very bullish on cryptocurrency, you can use Auto Trade to both hold cryptocurrency and realize gains from time-to-time.

Weaknesses of CML-I

  • Profit is not booked as frequently as with CML-A. This may result in drawdowns and oscillations in positions. 
  • Since fewer trades means less feedback, it may take longer to know if CML-I is working the way you want.
  • During sideways or down markets, CML-I may stay silent and keep you out of the market completely. While this may be a bonus as it reduces risk, it also means CML-I may miss some smaller opportunities.

As a general best practice, it’s good to select more than one strategy when using Auto Trade.

► Read the complete CML-I Overview

CML-T: Technical Deep Learning

CML-T performs a deep, novel, technical assessment of the market across numerous layers of market data. It utilizes this information to target extremely high-probability trades. These are trades that match patterns of highly successful trades in the past.

CML-T performs deep technical assessments of the markets across multiple layers to deliver high-probability trades.

CML-T performs complex equations but is not basing decisions on the output from our neural networks. Despite this, there are several areas machine learning contributes to this solution:

  • Deep learning uncovers key patterns and relationships in the market.
  • Optimization algorithms tune hundreds of parameters.

These findings and parameters are then curated into the CML-T solution.

Strengths of CML-T

CML-T seeks to issue very high probability trades. Its goal is to rapidly compound your trading funds.

Since it doesn’t rely on neural networks for buy and sell decisions, it can execute trades extremely quickly with little lag. This reduces trade slippage.

Weaknesses of CML-T

There may be extended periods that CML-T stays dormant. If it identifies unfavorable market conditions, it will avoid trading altogether.

► Read the complete CML-T Overview

CML-X: Experimental, Leading-Edge Strategies

As a platform, Crypto-ML is constantly evolving. We have team members dedicated to advancing our strategies through research and development.

These advances may become available to Crypto-ML customers in one of two ways:

  1. As an entirely new strategy.
  2. As an enhancement to existing strategies.

Prior to release, all changes go through extensive statistical testing. Once that is passed, the changes run against live data to evaluate real-world performance. At any given time, Crypto-ML has multiple concepts running in the background at this stage.

Oftentimes, as we are monitoring these evaluations, certain clear patterns arise across multiple tests indicating opportunities to buy or sell. When this arises, we may issue the corresponding signal to the CML-X trading strategy channel.

CML-X Strengths

CML-X represents the latest in technology from Crypto-ML. As such, you’re receiving signals that may be more sophisticated in their understanding of the markets. In addition, these signals will typically be built on top of the knowledge gained from our existing platform.

CML-X Weaknesses

Since CML-X is experimental in nature, there may be a higher likelihood of error or unanticipated behavior.

► Read the complete CML-X Overview


By choosing to have a certain percentage of your portfolio in Hold, you will be allocating a percentage of your funds to stay in Bitcoin or Ethereum. As your overall portfolio grows or shrinks, the amount of Bitcoin or Ethereum you hold will change correspondingly.

Leave a Comment

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Add predictive capabilities to your crypto investing.Join Crypto-ML for Free