Learn How To Trade

Our highly disciplined approach develops confidence in each trader, allowing them to become independent, patient, and profitable. We’ve been doing this for over a decade and believe in our approach to the point that every student that shows consistency and discipline during a 4 week simulated environment will be financed by Bitquants as a prop trader.

Trader Trading Course

The Bitquant Trading Program is an immersion course leading the novice through to accomplished trader.   Understanding the fundamentals of Blockchain technology,  crypto-currencies, technical analysis, capital allocation, risk management, trading psychology, backtesting, optimization and statistical arbitrage all of which are then applied on simulated environment before final exam. All materials are provided online accommodating students with busy schedules. Exam can be taken anytime, 2 weeks after registration. Show consistency and discipline and we will finance you as a prop trader. 

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Technical Analysis

The Technical analysis module is intended to introduce traders to the art of using market-generated data to forecast directional movements in prices. Technical analysis a tool, not an oracle. Many novice traders get caught up in the idea that they can find a few indicators which will pave their path to riches. If this were the case there would be far more millionaire traders. One of the key things novice traders need to learn is that technical analysis serves best as a risk management tool rather than a forecasting tool. When we can see the bigger picture in terms of whether something is overbought or oversold, or if a critical support level has been breached, we can get an idea of what is in the mind of traders who have existing positions and from that, gauge potential price movement. Technical tools allow us to determine rational placement of stops, the identification of targets, and risk/reward parameters.

Fundamental Analysis

The Fundamentals module is intended to introduce traders to the most pertinent macroeconomic data, explain the relevance of this data, and test a trader’s ability to quickly analyze and utilize this information to make informed trading decisions. Students with a background in economics may look upon this module not just as a review, but as a new way of thinking about economic fundamentals as they pertain to global foreign exchange and cryptocurrency markets. For students with limited or no exposure to economics and finance, this module will give them a firm foundation in economic fundamentals.

Market Profile & Trade Set Ups

A Market Profile is an intra-day charting technique (price vertical, time/activity horizontal) developed by J. Peter Steidlmayer, a trader at the CBOT. The objective of a  Market Profile is the profile display. This comes directly from the data itself, creating TPO’s ( Time Price Opportunity ) with for key element the initial balance ( the range and price location of the first hour of trading ) Even though mostly applied to fixed income futures , the system gives a very clear analysis of charts and potential trade set ups. Here the materials are given as they were developed by the author given cryptocurrency examples.

Psychology & Risk Management

This module is designed for trainees to understand the concept of establishing a risk system with risk reward ratios, managing entry and exit orders and supported with market and human psychology. This course is followed by a risk management article written to guide students in managing risk parameters and give the base to build a trading plan.

Build a Trading Plan


This module is designed for trainees to build their trading plan and organize their days. The live trading practice starts by pre market analysis and ends with post market critiques. The materials will help them build their plan, analyze their daily, monthly performances and get organized throughout the day .    

Dr. Ernest Chan

Head of Research and Training of Bitquants™

Ernie is the Head of Research of Bitquants™ . He supports students that participate in the Algorithmic Trading of Bitcoin course.  Ernie is also Managing Member of QTS Capital Management, LLC, a commodity pool operator and trading adviser. Ernie is the author of “Quantitative Trading: How to Build Your Own Algorithmic Trading Business” and “Algorithmic Trading: Winning Strategies and Their Rationale”, both published by John Wiley & Sons. He maintains a popular blog “Quantitative Trading” at epchan.blogspot.com.

Ernie teaches courses and workshops in trading and finance in London, Singapore, and elsewhere. He was appointed Adjunct Associate Professor of Finance at Nanyang Technological University and an Industry Fellow of the NTU-SGX Centre for Financial Education. He is also an adjunct faculty at Northwestern University’s Master of Science in Predictive Analytics program. More information about Ernie can be found at www.epchan.com.




Trainees receive support during and after the courses 



During the course, Dr. Ernest Chan will share his thoughts, ideas and sample strategies




Course can be held in our offices in Montreal, at your offices or online through Adobe Connect. Sign up for 1BTC & pass the exam when ready.



Algorithmic Trading of Digital Assets


The BitQuant workshop is a quantitative training course  developed for traders, system developers, analyst, risk, hedge fund and investment managers . This workshop focuses on the theories and practical implementation of algorithmic trading strategies as applied to Bitcoin exchange rate using MATLAB.


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This course focuses on the theories and practical implementation of algorithmic trading strategies as applied to bitcoin exchange rate using MATLAB.

Estimated Duration :
9 Hours (including exercises)
MATLAB ( No prior knowledge assumed )
Math Requirements: Basic College-Level Statistics
Market Data  and manuals will be provided.


  1. Bitcoin as foreign currency.
  2. Statistical characterization of BTC returns.
  3. Directional trading: mean reversion vs momentum.
  4. Triangle arbitrage.
  5. Cross-exchange arbitrage.


  1. Survey and comparison of available backtesting and trading platforms and software.
  2. Quick survey of syntax.
  3. Exercises: manipulating arrays.
  4. Using toolboxes. 


1. Autoregressive (AR) models.

  • Exercise: Stationarity condition for AR(p) models.
  • Exercise: Creating time-bars from trade ticks data.
  • Exercise: Testing BTC/CNY for stationarity using Econometrics Toolbox.
  • Exercise: Testing an AR(p) model for prediction.

2. Moving average (MA) and ARMA models.

  • Advantage of ARMA over AR.
  • Finding the best ARMA(p, q) model.
  • Exercise: Backtesting ARMA(p,q) trading model.

3. Autoregressive Integrated Moving Average (ARIMA) models.

4. Model complexity and out-of-sample returns.



  1. Bollinger bands.
  2. Exercise: Backtesting a Bollinger band strategy on BTC/USD.
  3. Importance, estimation, and minimization of transaction cost.


  1. What is order flow and why it is predictive of price movements?
  2. Methods for computing order flow.
  3. Exercise: Backtesting an order flow and why it is predictive of price movements?


  1. Exercise: Estimating inter-exchange arbitrage opportunities using bid-ask data.


At the end of the course, participants are expected to develop an understanding of the core concepts of Bitcoin quantitative trading, understanding Bitcoin exchange rates and appreciate the process of using mathematics and statistics to analyze profitability of their trading models.


While trading experience is preferred it is not essential.
Some programming experience is preferred


  • Finance, Economic and Statistics Students
  • Traders wishing to apply their market knowledge to the Bitcoin arena.
  • Anyone who aspires to become a Quant or Bitcoin Trader.


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