Investment Thesis: Leveraging Volume-Weighted Momentum and Rate-of-Change for Targeted Trend Capture

Overview

This strategy combines a Volume-Weighted Moving Average (VWMA) with a short-term Rate-of-Change (ROC) metric to identify and trade emergent trends in the market. By weighting recent prices with corresponding trading volumes, the VWMA offers a more robust picture of price action than a simple moving average. The N-day rate of change of this VWMA (in this case 6 days) serves as a momentum filter: only when momentum crosses a positive or negative threshold does the algorithm open positions, thereby filtering out sideways or noisy markets. This method seeks to maximize exposure during pronounced trends while minimizing false signals and risk in choppy environments.

Volume-Weighted Moving Average (VWMA)

Definition

• The VWMA is an average price that assigns greater weight to periods of higher trading volume.
• In this strategy, we calculate a 27-day VWMA using intraday (minute) data to capture a more granular view of market dynamics.

Rationale

• Enhanced Price Discovery: By including trading volume, VWMA reflects where the bulk of trading activity occurs, giving a clearer signal of true market consensus.
• Noise Reduction: Volume-weighting discounts low-volume price movements that might otherwise mislead a simple moving average strategy.

Implementation Detail

• We collect the past 27 days of minute-level bars (excluding the current day) to compute a daily VWMA. This number was found to be the highest returning and least volatile in our simulations.
• This also balances medium-term trend detection with the responsiveness of shorter-term data.

Rate-of-Change (ROC): The Momentum Trigger

Definition

• The strategy calculates a 6-day ROC on the daily VWMA: ROC= (Current VWMA - Past VWMA)/Past VWMA
• A minimum threshold (0.2% in this case) determines when momentum is significant enough to justify a position.

Rationale

• Momentum Filtering: By requiring ROC to exceed ±0.2%, the algorithm avoids trading in noisy, range-bound markets, reducing whipsaw losses.
• Clear Entry/Exit Signals:
  • Long Signal if ROC > +0.2%
  • Short Signal if ROC < –0.2%
  • No Position if ∣ROC∣ ≤ 0.2%

Adaptive Positioning

• When a long or short signal triggers, the algorithm adjusts position size to a fixed fraction of portfolio value (here, 40%). This approach ensures consistent risk-taking aligned with overall portfolio size and volatility in the market.

Risk Management and Execution

Threshold-Based Trading

• By establishing a momentum threshold, the strategy only participates when the likelihood of a genuine trend is higher. This helps avoid overtrading and reduces transaction costs.

Portfolio Allocation

• A set fraction of total portfolio equity (40%) is dedicated to each signal, maintaining disciplined exposure and preventing outsized bets on a single position.

Periodic Evaluation

• The strategy updates its signals daily at market close (16:00) to capture end-of-day liquidity and reduce intraday noise.
• If the signal falls back within the threshold, the position is liquidated, ensuring the strategy does not hold trades in neutral or ambiguous conditions.

Macroeconomic and Market Microstructure Considerations

Volume as a Sentiment Proxy

• In risk-on environments, volume often picks up alongside trending prices, reinforcing the VWMA signal.
• During uncertain macroeconomic conditions, volume spikes may produce abrupt changes in VWMA, which the ROC threshold helps filter out.

Liquidity and Volatility

• Volume-weighted metrics are most reliable in liquid markets (e.g., large ETFs like SPY or QQQ).
• The threshold logic naturally avoids trading during whipsaw price action, as volatile moves without accompanying volume won’t strongly shift the VWMA.

Event Risks

• Policy announcements or economic releases can impact volume and cause significant price gaps. The daily recalculation at the close helps adjust positions promptly.

Conclusion

This VWMA and Rate-of-Change strategy stands on strong quantitative and market microstructure foundations. By using volume-weighted data, the approach captures genuine shifts in supply and demand, filtering out low-volume noise. The ROC threshold ensures trading only occurs during meaningful momentum shifts, thus balancing return opportunities with risk control. Consequently, this framework can provide consistent, systematic exposure to emergent trends in equity markets, making it a compelling choice for investors seeking a disciplined, volume-aware momentum strategy.

(Note that this is a temporary strategy as our newly founded club works towards creating a more comprehensive, data-driven, and enduring systematic approach)