Types of trading algorithms for stock indices
There are several ways to build
an automated trading system or strategy. An algorithm is basically a trading strategy written in code.
Most algorithmic trading strategies fall into two categories: trend-following and mean reversion. Other
potentially successful trading strategies such as arbitrage, market making, high frequency trading (HFT)
and other statistics-based strategies are typically for professional quantitative traders. These might
not be suitable for beginners or intermediate traders. Large investment firms often use proprietary
automated trading platforms to execute their automated systems and strategies, as they might prioritize
minimum latency and high speed of execution. But for individual traders, it's perhaps better to focus on
classic trend-following and mean-reversion systems, as they demand less in terms of technology and
complexity.
Trend-following algorithms
Trend-following algorithms are
designed to spot and take advantage of current price trends. They use technical indicators such as
moving averages, relative strength, and momentum to determine the market’s direction and execute trades
accordingly. These algorithms work well in trending markets, but can suffer losses in unstable or
sideways markets.
Below is an example of the
historical performance of a simple trading algorithm, based on a crossover of moving averages, applied
to a Nasdaq stock index, represented in QQQ (an ETF from Invesco, based on the NASDAQ 100 index).
This strategy uses simple rules.
You hold a position if the price crosses the combination of moving averages.
In this chart you can see a
backtest of a trend-following strategy for QQQ (Nasdaq). Before applying your strategy to an
automated trading system, it is wise to backtest your strategies. Auto trading is not foolproof.
Source: Tradingview.com
Trend-following automated
trading systems are favored in auto trading because they capitalize on the momentum of price movements
in financial markets. However, like any strategy for trading, they have their limitations. Here are some
of the key challenges you might face using auto trading systems:
Whipsaws and false
signals
Trend-following trading systems
rely on technical indicators or moving averages to identify trends. But these automated trading systems
might give you false signals, in unstable or sideways markets. That could lead to losing trades when the
market suddenly changes direction. Such false signals are known as ‘whipsaws’.
Risk of continuous
losing streaks
Markets can sometimes experience
long periods of stability or irregularity. In such phases, trend-following systems can suffer from
prolonged losing streaks, which can be psychologically challenging for you as a trader.
Below is an example of applying
the same strategy, but for a much less trending (sideways) stock index, France’s CAC40.
Although the trade generated
some profit, it also produced many false entries, giving all that profit back. The strategy can be
adjusted or improved, but the general rule of thumb is that there should be a visible and extended trend
to profit from such a strategy.
Above, you can see the
application of a trend-following strategy for the CAC40 index. Source: Tradingview.com
Mean reversion algorithms
Mean reversion algorithms refer
to strategies that assume prices usually revert to their historical averages over time. As a trader, you
would use these algorithms to sell overvalued assets (priced more than they are worth) and buy assets
undervalued by the market (worth more than their current listed price). This method could potentially
help you make profits during unpredictable market periods when there’s no precise upward or downward
movement.
The following is an example of a
swing trading mean reversion strategy applied to the S&P 500 index (SPY ETF) on a 4-hour chart. You
can see that this strategy works better in a volatile market when prices are swinging up and down and
displaying some rotation. A price rotation is a sideways action where the price rotates around certain
price levels.
Here is an example of a
swing trading mean reversion strategy as applied to the S&P 500 index (SPY ETF) for a 4-hour
chart. Source: Tradingview.com
If you’re using a mean reversion
strategy, you can short the index when it reaches a new peak, and buy it when it hits a new low.
However, ‘real-world’ strategies might be more complex and involve some confirmations.
Like all strategies, mean
reversion trading has its limitations. Here are a few examples:
- False signals: sometimes,
prices might not return to their usual average point and this can lead to losses. You must
distinguish between genuine mean reversion trading opportunities and temporary fluctuations.
- Market trends and momentum:
Mean reversion strategies might not work well in a market strongly trending in one direction You may
experience losses if you keep trying to catch reversions that never happen.
- Drawdowns and large loss
risks: if a mean reversion trade goes against you and the price keeps straying further from the
average, your losses could pile up. It’s vital that you manage your risk and set appropriate stop
loss levels to limit your losses.