Daily Extremes – Significance of time

Analysing at which time daily market extremes are established shows the significance of the first and last hours of market action. See how different markets show different behaviour and see what can be learned from this analysis.

Probability of Extremes

A day of trading usually starts with a lot of fantasies for the future, then we try to survive the day and end it with a lot of hope for tomorrow. This psychological pattern can also be shown when analysing intraday market data. A high level of fantasies usually leads to a strong market movement, and thus market extremes can often be seen near the beginning or the end of the trading session.

distribution of intraday market extremes

distribution of intraday market extremes

The chart above shows the probability of making the daily high or low at a specific hour of trading. All analysis is done in German timezone.

The top left chart shows the time-extreme distribution for the SPY. It can clearly been seen, that the probability of marking the daily high or low is highest in the first and last hours of trading. The same is true for NBP, the chart on the upper right. Like SPY NBP shows a great likelihood to mark the daily high or low in the first or last hour of trading. Thus, if a new extreme value is shown later in the trading session, this will likely end in an extreme opposite the one build in the morning. A new high in the afternoon, after a weak start, leads to a higher high. This can be used for trading.

Trading the Time Probabilities

German DAX Index, the chart in the lower left corner, shows a different behaviour. It basically catches up with the late night American and overnight Asian movement in the morning, and then keeps within it’s firs hours range. No big hope at the end of the index session, as the american markets usually just open where the Europeans close. The DAX futures will show a different behaviour, as their trading time also incorporates the american trading.

EURUSD, the chart in the lower right corner, shows a different pattern. As it trades 24 hours a day, the significance of the different session can be seen on the chart. Over night only a few new highs or lows are marked. They are marked between 8 and 9 in the morning, when the European traders come into office, and in the afternoon between 3 and 5, when economic data is released.

Opening Range breakout trading

A simple opening range breakout strategy makes uses of the pattern shown above. It waits for the first third of the session, and takes a position if a new high or low is build afterwards.The trade is closed at the end of the day.

time based breakout strategy

time based breakout strategy

As NBP has a high probability of closing either at the high or low of the day an exit at the end of the session makes sense. Also waiting until the first third of the session is over is useful, as there is a high probability that either the high or the low has been established. If one of these points is crossed in the second part of the session, the probability is high that the market will keep it’s direction. Both observations are made on the time distribution diagram and translated into a trading strategy.

A link to a Tradesignal implementation of a  time based breakout strategy for SPY

A link to a different opening range breakout strategy from Perry Kaufmann (in German): opening range breakout strategy

S&P500 – when to be invested

The stock market shows some astonishingly stable date based patterns. Using a performance heat map of the S&P500 index, these patterns are easily found.

Date based performance

The chart below shows the profit factor of a long only strategy investing in the S&P500. Green is good, red is bad. The strategy is strictly date based. It always buys and sells on specific days of the month. Continue reading

Noisy Data strategy testing

Algorithmic trading adds noise to the markets we have known. So why not add some noise to your historic market data? This way you can check if your algorithmic trading strategies are fit for the future. Learn how to generate noisy data and how to test your strategies for stability in a noisy market.

Synthetic market data?

Continue reading

Factor investing in portfolio management

Factor investing has been around in portfolio management for some years. Based on algorithmic rules it became the big thing in trading and the ETF industry. But is there still some money to be made? Is small beta still smart or just beta? This article will give you a Tradesignal framework to test the factor investing ideas by your own. Continue reading