Decoding Money Flow within Markets to Anticipate Price Direction

Aug 12

I. Introduction

In the intricate world of financial markets, understanding the flow of capital between different assets is paramount for traders and investors aiming to anticipate price movements. Money doesn't move haphazardly; it often follows patterns and trends influenced by a myriad of factors, including economic indicators, geopolitical events, and inter-market relationships.

This article delves into the concept of money flow between markets, specifically analyzing how volume movements in one market can influence price directions in another. Our focus centers on two pivotal markets: the 10-Year T-Note Futures (ZN1!) and the Light Crude Oil Futures (CL1!). Additionally, we'll touch upon other significant markets such as ES1! (E-mini S&P 500 Futures), GC1! (Gold Futures), 6E1! (Euro FX Futures), BTC1! (Bitcoin Futures), and ZC1! (Corn Futures) to provide a comprehensive view.

By employing the Granger Causality test—a statistical method used to determine if one time series can predict another—we aim to unravel the nuanced relationships between these markets. Through this exploration, we aspire to equip readers with insights and methodologies that can enhance their trading strategies, particularly in anticipating price directions based on volume dynamics.

II. Understanding Granger Causality

Granger Causality is a powerful statistical tool used to determine whether one time series can predict another. While it doesn't establish a direct cause-and-effect relationship in the strictest sense, it helps identify if past values of one variable contain information that can predict future values of another. In the context of financial markets, this can be invaluable for traders seeking to understand how movements in one market might influence another.

Pros and Cons:

• Predictive Power: It provides a systematic way to determine if one market’s past behavior can forecast another’s, helping traders anticipate potential market movements.
  
• Quantitative Analysis: Offers a statistical basis for analyzing market relationships, reducing reliance on subjective judgment.
  
• Lag Dependency: The test is dependent on the chosen lag length, which may not capture all relevant dynamics between the series.
  
• Not True Causality: Granger Causality only suggests a predictive relationship, not a true cause-and-effect mechanism.
  

III. Understanding Money Flow via Granger Causality

The data used for this analysis consists of daily volume figures for each of the seven markets described above, spanning from January 1, 2018, to the present. While the below heatmap presents results for different lags, we will focus on a lag of 2 days as we aim to capture the short-term predictive relationships that exist between these markets.
Key Findings

The results of the Granger Causality test are presented in the form of a heatmap. This visual representation provides a clear, at-a-glance understanding of which markets have predictive power over others.



Each cell in the matrix represents the p-value of the Granger Causality test between a "Cause" market (row) and an "Effect" market (column). Lower p-values (darker cell) indicate a stronger statistical relationship, suggesting that the volume in the "Cause" market can predict movements in the "Effect" market.

Key Observations related to ZN1! (10-Year T-Note Futures):

• The heatmap shows significant Granger-causal relationships between ZN1! volume and the volumes of several other markets, particularly CL1! (Light Crude Oil Futures), where the p-value is 0, indicating a very strong predictive relationship.
  
• This suggests that an increase in volume in ZN1! can reliably predict subsequent volume changes in CL1!, which aligns with our goal of identifying capital flow from ZN1! to CL1! In this case.
  

IV. Trading Methodology

With the insights gained from the Granger Causality test, we can develop a trading methodology to anticipate price movements in CL1! based on volume patterns observed in ZN1!.

Further Volume Analysis with CCI and VWAP

1. Commodity Channel Index (CCI): CCI is a versatile technical indicator that when applied to volume, measures the volume deviation from its average over a specific period. In this methodology, we use the CCI to identify when ZN1! is experiencing excess volume.

Identifying Excess Volume:

• The CCI value for ZN1! above +100 suggests there is an excess of buying volume.
  
• Conversely, when CL1!’s CCI is below +100 while ZN1! is above +100, it implies that the volume from ZN1! has not yet transferred to CL1!, potentially signaling an upcoming volume influx into CL1!.
  

2. Volume Weighted Average Price (VWAP): The VWAP represents the average price a security has traded at throughout the day, based on both volume and price.

Predicting Price Direction:

• If Today’s VWAP is Above Yesterday’s VWAP: This scenario indicates that the market's average trading price is increasing, suggesting bullish sentiment. In this case, if ZN1! shows excess volume (CCI above +100), we would expect CL1! to make a higher high tomorrow.
  
• If Today’s VWAP is Below Yesterday’s VWAP: This scenario suggests bearish sentiment, with the average trading price declining.
  

Here, if ZN1! shows excess volume, we would expect CL1! to make a lower low tomorrow.

Application of the Methodology:

• Step 1: Identify Excess Volume in ZN1!: Using the CCI, determine if ZN1! is above +100.
  
• Step 2: Assess CL1! Volume: Check if CL1! is below +100 on the CCI.
  
• Step 3: Use VWAP to Confirm Direction: Compare today’s VWAP to yesterday’s. If it’s higher, prepare for a higher high in CL1!; if it’s lower, prepare for a lower low.
  

This methodology combines statistical insights from the Granger Causality test with technical indicators to create a structured approach to trading.

V. Case Studies: Identifying Excess Volume and Anticipating Price Direction

Case Study 1: May 23, 2024



Scenario:

• ZN1! exhibited a CCI value of +265.11
  
• CL1!: CCI was at +12.84.
  
• VWAP: Below the prior day’s VWAP.
  

Outcome:

• A lower low was made.
  

Case Study 2: June 28, 2024

Charts for this case study are at the top of the article.

Scenario:

• ZN1! exhibited a CCI value of +175.12
  
• CL1!: CCI was at -90.23.
  
• VWAP: Above the prior day’s VWAP.
  

Outcome:

• A higher high was made.
  

Case Study 3: July 11, 2024



Scenario:

• ZN1! exhibited a CCI value of +133.39
  
• CL1!: CCI was at +0.23.
  
• VWAP: Above the prior day’s VWAP.
  

Outcome:

• A higher high was made.
  

These case studies underscore the practical application of the trading methodology in real market scenarios.

VI. Conclusion

The exploration of money flow between markets provides valuable insights into how capital shifts can influence price movements across different asset classes.

The trading methodology developed around this relationship, utilizing the Commodity Channel Index (CCI) to measure excess volume and the Volume Weighted Average Price (VWAP) to confirm price direction, offers a systematic approach to capitalizing on these inter-market dynamics. Through the case studies, we demonstrated the practical application of this methodology, showing how traders can anticipate higher highs or lower lows in CL1! based on volume conditions observed in ZN1!.

Key Takeaways:

• Granger Causality: This test is an effective tool for uncovering predictive relationships between markets, allowing traders to identify where capital might flow next.
  
• CCI and VWAP: These indicators, when used together, provide a robust framework for interpreting volume data and predicting subsequent price movements.
  

Limitations and Considerations:

• While Granger Causality can reveal important inter-market relationships, it is not without its limitations. The test's accuracy depends on the chosen lag lengths and the stationarity of the data. Additionally, the CCI and VWAP indicators, while powerful, are not infallible and should be used in conjunction with other analysis tools.
  
• Traders should remain mindful of the broader market context, including economic events and geopolitical factors, which can influence market behavior in ways that statistical models may not fully capture. Additionally, effective risk management practices are crucial, as they help mitigate potential losses that may arise from unexpected market movements or the limitations of any predictive models.
  

When charting futures, the data provided could be delayed. Traders working with the ticker symbols discussed in this idea may prefer to use CME Group real-time data plan on TradingView: tradingview.com/cme. This consideration is particularly important for shorter-term traders, whereas it may be less critical for those focused on longer-term trading strategies.

General Disclaimer:
The trade ideas presented herein are solely for illustrative purposes forming a part of a case study intended to demonstrate key principles in risk management within the context of the specific market scenarios discussed. These ideas are not to be interpreted as investment recommendations or financial advice. They do not endorse or promote any specific trading strategies, financial products, or services. The information provided is based on data believed to be reliable; however, its accuracy or completeness cannot be guaranteed. Trading in financial markets involves risks, including the potential loss of principal. Each individual should conduct their own research and consult with professional financial advisors before making any investment decisions. The author or publisher of this content bears no responsibility for any actions taken based on the information provided or for any resultant financial or other losses.