The Volta strategy uses a volume-based indicator as its foundation, which distinguishes the strategy profile from most typical reversal strategies. It is a mean reversion strategy that waits for a quick pullback in an uptrend.
Inspiration
Trend reversal strategies are very effective in the stock market, where opportunities for profit often arise during retracements. In this article, we will discuss the details of the Volta strategy, including its historical results and recommended trading instruments.
Key Components
Detecting pullbacks in an uptrend. The essence of this strategy is the ability to detect quick pullbacks in an uptrend. The strategy utilizes typical reversal behavior in the stock market and momentary pullbacks, expecting the trend to continue.
Volume and price as fundamentals. The indicator used in the strategy helps identify overbought and oversold conditions in the market, taking into account both price and volume, allowing for potential reversal points to be found. The Volta strategy includes a specific application and 2 filters that turn on and off depending on prevailing conditions.
The Stockpicker mechanism searches and automatically selects stocks that meet the entry criteria.
Backtest 1, $ Money Management
In this variant, we invest a constant amount of $100k, which is divided by the maximum number of open positions (10). This results in a capital commitment of up to $10k per position.
We are testing the period of the last 30 years from 1994 to February 2025.
The backtest automatically selects stocks that meet the criteria from the Nasdaq 100 index. It is important to note that the list of stocks included in the index changed over the years, which is taken into account in the Stockpicker data (survivorship bias).
Invested capital: $100k
Number of positions: 10
Maximum investment per position: $10k
Test period in years: 30
Tested years: 1994-02.2025
Tested Index: Nasdaq 100
Equity chart for this test:
Basic statistics and results month by month:
Click the button to see the latest backtest:
Backtest 2, % Money Management
In this backtest, we are investing in a strategy that constantly uses 100% of the current capital (starting with $100k). This means that as the capital grows or decreases, the position value changes proportionally. The rest of the parameters remain unchanged.
The equity chart for this test looks as follows. The chart includes a benchmark - a thin yellow line at the bottom (!).
A red line on the chart is an Open Drawdown line.
Basic statistics resulting from the test:
Trading Strategy Analysis
Net profit and CAGR
The net profit of 117 million USD in the analyzed strategy is almost 60x higher than the Benchmark (S&P 500 Index in the form of the SPY ETF marked on the chart in yellow), which is above 2.12 million USD, translating to a CAGR of 25.6% vs 10.5%. This is the magic of Money Management and compound interest over 30 years.
Drawdown and Return/Drawdown ratio
The maximum Open Drawdown in the tested strategy was -22% compared to -55.19% in the benchmark, resulting in a better Return/Open Drawdown ratio of 9.3 vs 5.07, respectively. This means that the tested strategy is less risky and more stable because the maximum capital decline is smaller, leading to better risk management compared to the benchmark.
Exposure
The average exposure in the tested strategy was 50.60% vs 100% in the benchmark. The study was conducted on the underlying instrument, which is the Nasdaq 100 index stocks. Exposure is measured by a dedicated study, which you can read about here.
The tested strategy used on average half the capital, making it less exposed to market risks, and the available capital can be used in other strategies.
Winning percentage
The winning percentage in the analyzed strategy was 69.0%. This means that 69.0% of transactions resulted in profit, highlighting the effectiveness of the strategy in generating positive results, giving the user greater confidence in the frequency of achieving profits.
SL & TP
The strategy doesn't use a typical stop-loss and relies on an exit condition. But you can add an SL if it makes you more comfortable. Instead, diversifying positions within a single strategy and across the whole portfolio helps protect against the significant impact of a potential price change in one stock on the entire portfolio. Visit the stop loss order page.
Market regime
The strategy was tested in all basic market regimes and includes filters implemented based on this. Read more about market regimes.
Trading Costs
Trading costs and slippage were taken into account in the backtests. You can check our last research about trading costs using Alpaca Broker here. With a diversified portfolio of stocks and strategies, transaction costs can determine your profit or loss, so take the time to thoroughly test and choose a broker.
Robustness
We tested robustness by conducting all possible stock transactions with a maximum of 100 open positions for the period from 1994 to February 2025. This included 83,044 transactions for the S&P 500 index and 109,180 transactions for the Russell 1000, both at %MM. Our strategy successfully passed parameter modification tests. We believe that fewer parameters lead to a more robust strategy. Therefore, we strive to minimize the number of parameters, selecting only those that significantly impact the strategy's effectiveness and align with its character.
S&P500 max transactions: 83'044
Russell 1000 max transactions: 109'180
Recommended instruments
The recommended primary instrument for this strategy on Algocloud Stockpicker is the companies in the Nasdaq 100 index, which have shown the best historical results. However, the strategy also shows stable results with stocks in the S&P 500.
Primary instrument: Nasdaq 100
Supplementary instrument: S&P 500
Pattern Day Trader
Statistically, the strategy closes 14.5% of trades on the same day, meeting the criteria for a Pattern Day Trader (PDT). This means that a real account with a minimum of $25k is required for the entire portfolio. Pattern Day Trader
Correlation
To check the correlation of the strategy with others, visit the correlations page.
Summary & Strengths and Weaknesses of the strategy
Over the course of 30 years, the analyzed strategy has not had a single losing year, which is a phenomenal result for a Stockpicker type strategy.
Strengths of the strategy:
Exceptional profits. In the analyzed strategy, Net Profit was $117 million USD, while the Benchmark achieved above $2.12 million USD. The CAGR was 25.6%, significantly higher than the Benchmark's 10.5%.
Low drawdown. The Max Drawdown in the analyzed strategy was 22% compared to 55.19% in the Benchmark. This shows that the strategy is much less risky and more stable.
High winning percent. 69.0% of transactions ended in profit, highlighting the effectiveness of the strategy and providing a high level of comfort for users.
Robustness. The strategy was tested on the S&P500 and Russell 1000 indices, achieving a maximum of 83'044 and 109'180 transactions respectively.
Weaknesses of the strategy:
Capital commitment. The strategy requires a relatively large trading account ($25k+ Account), because of percentage of transactions ending on the same day, which is 14.5%. This may lead to meeting the Pattern Day Trader requirements mentioned above.
Summary
The Stockpicker type strategy demonstrates very high efficiency, low risk, and great stability over the long term. Such strategies can be the main drivers of of the stock market portfolio.
What you receive in the package for this strategy:
An e-book presenting detailed rules and results of the strategy.
An SQX file ready to use on platforms like Algocloud and StrategyQuant.
Pseudocode describing all rules in an easy-to-understand manner.
Disclaimer
The results obtained from historical data do not guarantee future outcomes. The effectiveness of a strategy can change over time. Backtesting is a tool that allows for the analysis and evaluation of an investment strategy based on historical data. Various factors, such as market changes or economic conditions, can influence the effectiveness of a strategy over time.
Investing always involves risk. This material is not investment advice. We share our experience and algorithms for educational purposes. We make efforts to ensure that our algorithms are error-free, but neither we nor the tools we use guarantee the absence of technical issues. Any decisions to use a particular strategy are made at your own risk and should be preceded by careful understanding and verification. You should always carefully consider your investment goals and risk tolerance before making investment decisions.
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