Over the past few years I have found that the best way to improve the quality of my discretionary trading software was to create an algorithmic trader that used the data points provided by the Pipnotic algo trading engine and use them to place trades. I have had multiple algo trading projects over the years, including a few based on currency strength (read more about that here) which helped me improve the usability and quality of the software. In 2016 I started packaging my supply and demand testing utility that I have been using for many years to help validate the quality of supply and demand and turned it into a standalone algo trader, the results of which, when used with discretion, are very positive. That’s right, the POOLS project was born as a tool to help me identify valid combinations of parameters for my discretionary supply and demand indicator, which visually shows you where to buy and sell what ever the indicator is attached to.
What is algo trading?
Here is the definition provided by Wikipedia:
Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume to send small slices of the order (child orders) out to the market over time. They were developed so that traders do not need to constantly watch a stock and repeatedly send those slices out manually. Popular “algos” include Percentage of Volume, Pegged, VWAP, TWAP, Implementation shortfall, Target close. In the twenty-first century, algorithmic trading has been gaining traction with both retail and institutional traders.
widely used by investment banks, pension funds, mutual funds, and hedge funds
It is widely used by investment banks, pension funds, mutual funds, and hedge funds because these institutional traders need to execute large orders in markets that cannot support all of the size at once.
The term is also used to mean automated trading system. These do indeed have the goal of making a profit. Also known as black box trading, Quant or Quantitative trading, these encompass trading strategies that are heavily reliant on complex mathematical formulas and high-speed computer programs.
Black box trading, Quant or Quantitative trading
Such systems run strategies including market making, inter-market spreading, arbitrage, or pure speculation such as trend following. Many fall into the category of high-frequency trading (HFT), which are characterized by high turnover and high order-to-trade ratios. As a result, in February 2012, the Commodity Futures Trading Commission (CFTC) formed a special working group that included academics and industry experts to advise the CFTC on how best to define HFT. HFT strategies utilize computers that make elaborate decisions to initiate orders based on information that is received electronically, before human traders are capable of processing the information they observe. Algorithmic trading and HFT have resulted in a dramatic change of the market microstructure, particularly in the way liquidity is provided
Visit Wikipedia and read the entire article here.
The role liquidity plays in the way a given market moves
Understanding supply and demand is about understanding the role liquidity plays in the way a given market moves. It is a fascinating concept and one the understanding of which will lead you to make better trading decisions. Understanding what it is that makes the price of an asset move will simply make you a better trader, as it will bring your attention to something you aren’t seeing when you look at a price chart. Combine this with market cycles and I think you’ll be stacking the odd very much in your favour, which, in the trading world is what it’s all about.
I have compiled a few videos over the years that help illustrate what I’ve been up to – and here’s a webinar I did for FX Street that presents the process and some of the results.
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