What’s What? Manual Trading vs. Algo-Trading vs. Auto-Trading

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The Spectrum: Manual Trading vs. Algo-Trading vs. Auto-Trading

The rise of weather-dependent renewable energy sources is changing the way we are trading power. As weather forecast accuracy increases with delivery approaching, short-term power markets are becoming more and more liquid and volatile. 

This development leads power traders to accelerate and improve decision making by digitizing the short-term power trading value chain:

1. Data (connection to data providers) — 2. Intelligence (price and volume decision making) — 3. Execution (order placement).

Depending on the degree of automation along the value chain, trades are either made in a manual, algorithmic or automated way. Although very different, the latter two are often confused or used interchangeably. 

So, let’s zoom in to understand the idea behind different trading approaches and learn which role technology plays in modern power trading.

Difference between Manual Trading, Algo-Trading and Auto-Trading

Manual, algorithmic and automated trading approaches

Manual Trading

Data (automated) — Intelligence (manual) — Execution (manual)

Manual trading is characterized by human decision making, based on visual data inspections and manual order execution.

However, even manual traders are reliant on technologies and data infrastructures that serve as the basis for the visual output to help them make intelligent trading decisions.

The most basic way to trade is to purchase data from external providers and analyze it via spreadsheets or third-party user interfaces. Some sophisticated manual traders go further and visualize data insights by building proprietary dashboards.

In practice, traders analyze the received input manually, usually looking at multiple screens, displaying information regarding wind and solar forecasts, last observed and average spreads, day-ahead volumes, price data, etc. Based on this input, traders make decisions and execute trades.

And the execution of trades matters due to the dynamics of the order book. Trading a relatively large volume (compared to the total volume available in the order book) can be a challenging endeavor. 

For example, in an order book where the first 1 MWh can be bought at  90 €/MWh, the subsequent  10 MWh might cost 95 €/MWh, the subsequent 100 MWh 110 €/MWh and so on. This effect increases exponentially. Spreading volumes over time can dampen the volatility and risk. As a result, if you buy 1 MWh at a time, you will likely get a better deal. 

A manual approach therefore forces traders to have a continuous focus on both trading decisions and order executions — a task that gets significantly harder as market liquidity and volatility is growing.  

Compared to algo- and auto-trading, manual trading involves the lowest investment in technology. However, this approach comes with drawbacks such as:

  • relatively long reaction times to newly available information
  • non-systematic (often intuition-based) decision-making
  • tedious manual execution in algo-trader driven order books

Algorithmic Trading

Data (automated) — Intelligence (manual) — Execution (automated)

Many people associate non-human trading with the term “algo-trading”. This is a misconception. Algo-trading (or algorithmic trading) is a technique to automate and optimize the final step of the power trading value chain — order execution — by means of simple if-then rules and/or algorithms. 

An algo-trading software requires certain order parameters (e.g. stop-loss and take-profit limits) as inputs and creates a range of orders as outputs with the aim to maximize the return on a given (manual) trading decision. To accommodate for this, algo-trading software providers usually offer a rather simple interface and standard functionalities.

Many power traders have evolved from manual to algorithmic trading, meaning that trading decisions are still made by people who communicate price, volume and buy/sell parameters to a pre-configured algo-trader to automate order execution.

Automated execution is obviously more efficient than manual order submission: it saves a trader’s time and serves as protection during single extreme market events.

Automated Trading

Data (automated)Intelligence (automated)Execution (automated)

Auto-trading is the peak of technologization in trading. It requires no human involvement. 

From predicting traded volumes and market prices based on complex data to making intelligent decisions to smart order execution: auto-trading is an end-to-end approach to eliminate manual processes in trading.

An event-driven auto-trading engine runs continuously and reacts every time a data update is received: most recent data is analyzed automatically to make a price/volume decision by using a prediction algorithm and sending orders to the market autonomously. 

Very few early-adopting trading companies are developing and testing fully automated trading engines in weather-based power markets. However, most are holding back because an end-to-end automation approach requires sizable investments in technology and the scalability thereof. 


In response to the complexity of short-term power markets, many participants are relying on a combination of manual and algorithmic trading: the percentage of orders executed by algorithms in European power exchanges is growing aggressively. An increasing number of traders are meanwhile adopting semi-automated approaches to keep up with extreme market volatility and accelerated data-driven decision making. 

The market is therefore steadily moving towards the highest level of trading automation — autonomously running trading engines able to incorporate and digitize every stage of a trade life cycle from data processing to decision making to order execution.

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