Seasonal Patterns And Boston Forex Trading Profits

Seasonal Patterns And Boston Forex Trading Profits – We applied corrective AI (Chan, 2022) to a trading model that outperforms the one-day Forex trading cycle. Breedon and Ranaldo (2012) observed that foreign currencies depreciated against the US dollar during local business hours and appreciated during local US dollar business hours. We first tested the results of Breedon and Ranaldo against the latest EURUSD data from September 2021 to January 2023, and then applied AI correction to this trading strategy to achieve a significant increase in performance.

Breedon and Ranaldo (2012) described a trading strategy that shorted EURUSD during European business hours (3 PM ET to 9 AM ET, where ET represents local time in New York, day deposits) and short EURUSD in purchased US working time (11). AM ET to 3 PM ET). This is because large-scale institutional purchases of US dollars are made during European business hours to pay global invoices, and vice versa during US business hours. Therefore, this effect is also called the “facture effect”.

Seasonal Patterns And Boston Forex Trading Profits

There is some supporting evidence for time-of-day patterns in various measures of the foreign exchange market, such as volatility (see Bail and Bollerslev (1991) or Andersen and Bollerslev (1998)), volatility (see Hartmann (1999), or Ito and Hashimoto (2006) )) and return (see Cornett (1995) or Ranaldo (2009)). Basically, local currencies depreciate during their local business hours for each of these measures and appreciate during US business hours.

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Figure 1 below depicts the hourly average return for each hour per day for the period from 2019-01-10 17:00 ET to 2021-09-01 16:00 ET. It reveals the EURUSD reversal pattern. The pattern of returns on “working hours” described above is broadly consistent with the popular “invoice effect” hypothesis. Returns decrease during the European period and increase during the US period.

Since this strategy was published in 2012, it allows enough time for actual out-of-sample testing. We collected 1-minute EURUSD data from the Electronic Brokerage Service (EBS) and performed a back-test for the out-of-sample period from October 2021 to January 2023. The Sharpe ratio of the strategy during this period is 0.88 with the average annual return. 3.5% and maximum -3.5%. Apparently, the alpha strategy has endured. (For the purpose of this article, no transaction costs are included in the test, as our sole purpose is to compare performances with and without AI correction, not to determine whether this trading strategy is viable in live production.)

Figure 2 below shows the equity curve (“increase $1”) of the strategy in the period outside of the above sample. The cumulative return for this period is slightly less than 8%. We call this the “Primary” trading strategy for reasons that will become clear below.

Let’s say we have a trading model (like the initial trading strategy described above) to place the side of the bet (long or short). We only need to learn the size of the bet that includes the possibility of no bet at all (size zero). This is a situation that practitioners face regularly. To figure it out, a machine learning (ML) algorithm is trained. To emphasize, we don’t want the ML algorithm to learn or predict the edge, just tell us what the appropriate size is.

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We call this problem meta-tagging (López de Prado, 2018) or adaptive AI (Chan, 2022) because we want to build a secondary ML model that learns how to use the primary trading model.

We will learn an ML algorithm to calculate the “Probability of Profit” (PoP) for the next minute. If PoP is greater than 0.5, we set the bet size to 1; otherwise, we set it to 0. In other words, we take the step function as a bet size function that takes PoP as input and gives bet size as output, with a threshold set at 0.5. This bet sizing function decides whether to bet or pass, a purely binary prediction.

The training period was from 2019-01-01 to 2021-09-30, while the out-of-sample period was from 2021-10-01 to 2023-01-15, which is similar to the out-of-sample period we reported for the trading strategy. primary The model used to train the ML algorithm is made using the Corrective AI API (CAI) with over a hundred pre-designed input features (predictors). The main learning algorithm is the gradient decision tree.

After applying the corrective AI, the Sharpe ratio of the strategy during this period is 1.29 (increase of 0.41), with an average annual return of 4.1% (increase of 0.6%) and a maximum of -1.9% (decrease of 1.6 ) is %). The alpha strategy has been significantly improved.

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The equity curve of the secondary filtered Corrective AI signal can be seen in the figure below.

The features used to train the AI ​​corrective model include technical indicators generated from indices, stocks, futures and options markets. Many of these features are built using Algoseek’s high-frequency futures and stock data. A further discussion of these features can be found in (Nautiyal & Chan, 2021).

By applying corrective AI to the day’s initial strategy, we were able to improve the Sharpe ratio and reduce the drawdown during the out-of-sample testing period. This is consistent with observations in the literature on meta-tagging for our initial strategies. The AI ​​correction model’s signal filtering capabilities improve performance in specific scenarios.

We are grateful to Chris Bartlett of Algosec who generously provided much of the high-frequency data for our feature engineering in the AI ​​correction system. We also thank Pawan Dutt for his help with feature engineering and Jay Sukumar for his help using the CAI API. Finally, we thank Eric McDonald and Jessica Watson for their contributions in explaining this technology to customers.

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Nautiyal, A., and Chan, E. (2021). New additions to the factor zoo. . Accessed on February 28, 2023 from https:///new-additions-to-the–factor-zoo/Forex trading is a dynamic and complex market that offers a wide range of opportunities for investors around the world. To succeed in this fast-paced environment, traders must be equipped with the right strategies. One strategy that is often overlooked is using seasonal patterns. Forex trading and seasonality refer to recurring price patterns that are influenced by specific times of the year, months, weeks, and even specific days. In this article, we will explore the concept of seasonality in forex trading, its importance and how traders can effectively use it to improve their trading performance.

Seasonality in currency trading refers to recurring and predictable patterns in price movements that occur at specific times. This seasonal pattern can be influenced by various factors such as economic events, cultural and social events, trading volume and behavior of market participants. Unlike other trading strategies that focus on technical or fundamental analysis, seasonality takes into account the temporal aspect of the market.

Economic events and the calendar: Key economic indicators, interest rate decisions and other planned economic events can significantly affect currency pairs and create seasonal patterns.

Cultural and social events: Holidays, festivals and other cultural events can affect trading volume and market behavior, leading to seasonal trends.

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Trading volume and behavior of market participants: During certain seasons of the year, market participants, such as institutional investors or traders in specific regions, may exhibit regular trading patterns that contribute to seasonality in the currency markets.

Seasonal patterns provide traders with statistical advantages because historical data often show the repetitive nature of price movements at specific times. By recognizing and understanding these patterns, traders can predict potential market behavior and improve their trading decisions.

Not all currency pairs are equally affected by seasonality. Some currency pairs show greater seasonality due to their relationship to specific economic or cultural factors. For example, commodity currencies such as the Australian dollar (AUD) may reflect seasonality with agricultural cycles or demand for natural resources. Recognizing these specific seasonal patterns can help traders identify trading opportunities and align their strategies accordingly.

To effectively use forex seasonality, traders must conduct a thorough seasonal analysis. This involves gathering historical data, preferably over several years, and identifying recurring trends and seasonal patterns. Various technical indicators and graphical tools can be used to visualize and confirm seasonal signals.

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Based on the insights gained from seasonal analysis, traders can develop specific trading plans tailored to seasonal patterns. This includes timing entries and exits based on seasonal trends, managing risk by setting appropriate stop loss orders, and setting profit targets that align with expected seasonal movements.

Seasonality should be used in conjunction with fundamental analysis to enhance trading decisions. By seasonally aligning with key economic events and news releases, traders can take advantage of the combined effects of fundamental factors and seasonal patterns that lead to high-quality trading setups.

While forex seasonality provides valuable insight, unexpected events or market anomalies can disrupt seasonal patterns. Traders must adapt and adapt their strategies to account for changing market conditions and ensure that they do not rely solely on historical patterns.

Proper risk management is important in forex trading, including seasonal forex trading. Traders should set realistic profit targets and set appropriate loss levels to manage potential risks associated with seasonal trading. This ensures that losses are contained and potential profits are maximized.

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