Should You REALLY Trade on FOMC, CPI, PPI, & Jobs Report Days?

Key Concepts

• Event Filtering in Backtesting: The ability to exclude specific economic or market events from trading strategy backtests.
• Market-Moving Events: Economic releases and scheduled events that are known to cause significant price volatility in financial markets. Examples include CPI, PPI, PCE, Non-Farm Payrolls, FOMC meetings, and Triple Witching.
• Strategy Performance Metrics: Key indicators used to evaluate trading strategies, such as P&L (Profit and Loss), Max Drawdown, Profit Factor, and Win Rate.
• Volatility Premium: The idea that traders are compensated with higher potential profits for taking on increased risk associated with volatile market conditions.

New Backtesting Capabilities at Optional Alpha

Optional Alpha has released a new update to its backtesting platform, significantly expanding the range of events that traders can filter out of their strategy simulations. This feature addresses a common request from users who want to avoid trading during periods of high market volatility caused by specific economic announcements.

Previously Available Filters

Previously, the platform offered the ability to filter out FOMC (Federal Open Market Committee) meetings, which was a standard feature.

Expanded Event Filtering Options

The new update introduces a comprehensive list of additional events that can be excluded from backtests. These include:

• Inflationary Data:
  • CPI (Consumer Price Index)
  • PPI (Producer Price Index)
  • PCE (Personal Consumption Expenditures)
• Employment Data:
  • Non-Farm Payrolls (Jobs Report)
• Market Expirations:
  • Triple Witching (simultaneous expiration of stock index futures, stock index options, and stock options)
• Other Significant Events:
  • FOMC Meetings (still included)
• Novelty Filters:
  • Full Moon (added due to user requests, though presented as a more whimsical option)

The underlying logic for filtering these events is the belief that they are "market-moving" and lead to increased volatility, which some traders prefer to avoid.

Backtesting Methodology and Examples

The video demonstrates the process of adding these new filters to existing backtests.

1. Creating a Variation: To test the impact of event filtering, a new variation of an existing backtest is created by clicking "Compare" and then "Add Variation."
2. Applying New Filters: Within the variation settings, a new "Event Category" is available, allowing users to select specific events to filter out.

Example 1: Short Put Spread Strategy

• Baseline: A traditional put spread strategy was run without any event filtering (implicitly, FOMC was already filtered out in the original comparison).
• Variation 1: FOMC + Jobs Report: The strategy was backtested while filtering out both FOMC meetings and Non-Farm Payrolls.
  • Result: This variation showed "effectively the same" performance as the baseline, with no meaningful difference.
• Variation 2: Skip All Events: The strategy was backtested while filtering out all major market-moving events.
  • Result: This variation "significantly underperformed" across the board. However, it did result in a "smaller max drawdown," indicating less peak-to-trough decline, though not an avoidance of drawdowns altogether. The conclusion drawn is that for this specific strategy, avoiding all events was detrimental to performance, despite potentially improving stress levels.
• Variation 3: Avoid Full Moons: A test was conducted to filter out only full moon cycles.
  • Result: Surprisingly, this variation "actually did better" in terms of performance compared to avoiding all events. However, it also exhibited the "largest draw down," suggesting a trade-off between performance and risk.

Example 2: ORB (Opening Range Breakout) Strategy

The presenter then analyzes their own ORB strategy backtests:

• FOMC Only: Filtering out only FOMC meetings.
• FOMC and Jobs Report: Filtering out both FOMC and Non-Farm Payrolls.
  • Result (FOMC Only vs. FOMC + Jobs Report): Both variations showed very similar performance, with the FOMC + Jobs Report line being nearly identical to the FOMC Only line.
• Avoid All Events: Filtering out all major events.
  • Result: This variation "underperformed" significantly compared to the baseline.
• Exclude FOMC: Filtering out everything except FOMC meetings.
  • Result: This also "underperformed."

Example 3: Personal Trading Portfolio Strategies

The presenter shares results from their own live trading strategies on their Zerodha portfolio, which are typically entered later in the day and already have FOMC avoidance built-in.

• Strategy 1: 2:30 Iron Condor (Long-Term):
  • Test: Avoid all events.
  • Result: "Dramatically underperforms." The presenter concludes they are "better off trading through all of these events than avoiding all of them."
• Strategy 2: 150 Iron Butterfly (40% Profit Target):
  • Test: Avoid all events.
  • Result: "Underperformed." While there were periods of better performance, "net net at the end of the day, it actually was a little bit better performer to just trade through all of the events."
• Strategy 3: Newer, Wider Iron Condor (2:30 PM Entry):
  • Test: Avoid all events.
  • Result: The disparity was "pretty significant." The presenter highlights that avoiding too many trading opportunities leads to a substantial difference in P&L, as traders are "getting paid for the volatility and the risk."
• Strategy 4: One Day to Expiration (1DTE) Iron Condor:
  • Test: Avoid all events.
  • Result: This was a "meaningful one" because these strategies often trade through events announced after market close or before the next day's open. The disparity in P&L was "pretty significant," while the maximum drawdown did not show a large difference. The profit factor did not improve, and the win rate only slightly improved without being meaningful.

Key Arguments and Perspectives

The central argument presented is that while the intention to avoid volatile market-moving events is understandable, for many strategies, especially those tested by the presenter, filtering out these events leads to underperformance. The evidence suggests that traders are often compensated for the increased risk and volatility associated with these events through higher potential profits.

• Argument: Avoiding market-moving events might improve sleep quality or reduce stress.
• Counter-Argument (based on data): From a strategy performance standpoint, avoiding these events can be detrimental, leading to significantly lower P&L.
• Argument: Volatility implies risk.
• Supporting Evidence: The presenter's backtests show that by trading through these events, strategies often capture the "premium" associated with that volatility, leading to better overall returns.

Technical Terms and Concepts Explained

• Backtesting: The process of simulating a trading strategy on historical data to assess its potential profitability and risk.
• Short Put Spread: A vertical spread strategy involving selling a put option and buying another put option with a lower strike price, both with the same expiration date. It's a bullish to neutral strategy.
• FOMC (Federal Open Market Committee): The monetary policymaking body of the Federal Reserve System, responsible for setting interest rates and managing the money supply. FOMC meetings are closely watched for potential market-moving announcements.
• CPI (Consumer Price Index): A measure that examines the weighted average of prices of a basket of consumer goods and services, such as transportation, food, and medical care. It is a key indicator of inflation.
• PPI (Producer Price Index): A measure of the average change over time in the selling prices received by domestic producers for their output.
• PCE (Personal Consumption Expenditures): A measure of the prices that consumers pay for goods and services. It is considered a broader inflation measure than CPI by the Federal Reserve.
• Non-Farm Payrolls: A monthly report by the U.S. Bureau of Labor Statistics that measures the number of U.S. workers, not including farm laborers, private household employees, or unpaid volunteers. It is a key indicator of the health of the labor market and the economy.
• Triple Witching: A day when stock index futures, stock index options, and stock options all expire on the same day. This can lead to increased trading volume and volatility.
• Max Drawdown: The largest peak-to-trough decline in the value of an investment or trading account over a specific period.
• P&L (Profit and Loss): The net financial gain or loss from a trading strategy or investment.
• Profit Factor: A ratio of gross profits to gross losses. A profit factor greater than 1 indicates profitability.
• Win Rate: The percentage of trades that result in a profit.
• ORB (Opening Range Breakout): A trading strategy that involves identifying a price range during the initial trading period (e.g., the first 30 minutes) and entering a trade when the price breaks out of that range.
• Iron Condor: A neutral options strategy that involves selling an out-of-the-money (OTM) call spread and an OTM put spread simultaneously. It profits from low volatility.
• Iron Butterfly: A neutral options strategy that involves selling an at-the-money (ATM) straddle and buying OTM options to define risk. It also profits from low volatility.
• 1DTE (One Day to Expiration): Options contracts that expire on the next trading day. These are often highly speculative and can be very sensitive to price movements.

Logical Connections Between Sections

The video progresses logically from introducing a new feature (event filtering) to demonstrating its application with various strategies and then drawing conclusions based on the backtesting results. The initial explanation of the feature sets the stage for the subsequent examples, which serve as evidence for the presenter's perspective on the efficacy of avoiding market-moving events. The comparison between different types of events (e.g., FOMC vs. Full Moon) highlights the nuanced impact of filtering. Finally, the analysis of personal trading strategies reinforces the main takeaway that for many strategies, trading through these events is more profitable.

Data, Research Findings, or Statistics

While specific numerical data points for every single backtest are not explicitly stated in numerical form (e.g., "profit increased by X%"), the video relies on visual comparisons of performance charts and qualitative descriptions of results:

• "significantly worse across the board"
• "smaller max draw down"
• "gave it up significantly in performance"
• "underperforms"
• "actually did better"
• "largest draw down"
• "underperforming"
• "disparity here is pretty significant"
• "big disparity in P&L"
• "win rate slightly improved, but not by any meaningful amount"

The presenter emphasizes that the "P&L difference expected from these two strategies is significant."

Conclusion and Synthesis

The core takeaway from this video is that while the ability to filter out market-moving events in backtesting is a valuable new feature, traders should exercise caution and rigorously test its impact on their specific strategies. For many of the strategies demonstrated, including the presenter's own live trading strategies, avoiding events like CPI, Non-Farm Payrolls, and even FOMC meetings led to underperformance and a significant reduction in potential profits. The presenter suggests that traders are often compensated for the volatility and risk associated with these events, and by avoiding them, they miss out on profitable trading opportunities. The recommendation is to "back test like crazy" and test these filters on individual strategies before making decisions based on them. While FOMC and jobs reports might warrant consideration for avoidance in some cases, a blanket avoidance of "all events" is generally not recommended based on the presented evidence.