Boost Your Trading Efficiency: Scaling In for Better Average Prices

What is Scaling Into A Trade?

Scaling-in is a trading technique where a trader enters a position gradually, in smaller increments, rather than entering the full position size all at once. 

When scaling-in, a trader divides their intended total position size into smaller lots and enters these lots at different price points or time intervals. The opposite of scaling in is taking a full position all at once, also known as "going all-in."

Let's look at a detailed example to illustrate how scaling-in works:

Suppose an algorithmic trader has developed a trading system that generates a buy signal for Stock XYZ. The trader's usual position size for this stock is 1000 shares, but they decided to scale into the position to manage risk and capture a better average entry price.

The trader's scaling plan is as follows:

1. Buy 200 shares at the market price when the buy signal is generated
2. Buy an additional 200 shares if the price drops by 1% from the initial entry price
3. Buy another 200 shares if the price drops by 2% from the initial entry price
4. Buy the final 400 shares if the price drops by 3% from the initial entry price

Benefits of Scaling Into A Position

One of the biggest challenges in trading is entering a position at the right time and price. Even with a well-designed trading system, it's impossible to consistently predict the exact bottom for a buy or the precise top for a sell. This is especially true in volatile or choppy markets, where prices can whipsaw up and down, making it difficult to pinpoint the ideal entry.

Scaling in helps mitigate this timing risk by spreading the entry over multiple price points. Instead of going all-in at a single price, the trader can buy or sell in smaller increments as the market moves. This approach offers several benefits:

1. Cost Averaging: Scaling in can help in averaging down the entry price when the price moves against the position initially. Adding to the position at lower prices reduces the average cost of the total position, potentially leading to better outcomes when the market rebounds.

2. Reduced Risk: If the initial entry is poorly timed (e.g., the market moves against your initial position), the impact is reduced because only a portion of the full position is affected. You can then add to your position at a better price, thus improving the average cost of your position. By scaling in, algorithmic traders can reduce the risk of entering a trade at a single, potentially unfavorable price. This approach helps reduce drawdowns.

Based on the scaling plan from the previous section, let's see how this plan might play out in two different scenarios.

Scenario 1:

• Stock XYZ is trading at $50 when the buy signal is generated
• The trader buys 200 shares at $50
• The price drops to $49.50 (1% drop), and the trader buys another 200 shares
• The price then rises and never looks back, closing the day at $52

In this scenario, the trader's average entry price is $49.75 ((200 * $50 + 200 * $49.50) / 400). By scaling in, the trader bought 400 shares at a better average price than if they had gone all-in at $50.

Scenario 2:

• Stock XYZ is trading at $50 when the buy signal is generated
• The trader buys 200 shares at $50
• The price drops to $49.50 (1% drop), and the trader buys another 200 shares
• The price drops further to $49 (2% drop), and the trader buys an additional 200 shares
• The price then drops to $48.50 (3% drop), and the trader buys the final 400 shares
• The stock closes the day at $48

In this scenario, the trader's average entry price is $48.75 ((200 * $50 + 200 * $49.50 + 200 * $49 + 400 * $48.50) / 1000). Although the stock closed lower than the initial entry price, the trader accumulated the full 1000 shares at a better average price by scaling in.

Now, let's compare this to an all-in approach in the same scenario:

Scenario 2 (All-In):

• Stock XYZ is trading at $50 when the buy signal is generated
• The trader immediately buys 1000 shares at $50
• The price drops to $49.50, then to $49, and finally to $48.50
• The stock closes the day at $48

The trader's average entry price in the all-in approach is $50, as they bought all 1000 shares at the initial price.

The difference in the average entry price between the two approaches is $1.25 per share ($50 - $48.75). This may seem like a slight difference, but it can significantly impact the trader's overall profitability, especially when dealing with larger position sizes or multiple trades over time.

In this scenario, scaling-in allowed the trader to take advantage of the falling price and accumulate shares at a lower average cost. This means that if the price starts to move back up, the trader will reach their breakeven point sooner and potentially realize larger profits.

On the other hand, the all-in approach left the trader fully exposed to the price drop, resulting in a higher average entry price and a larger unrealized loss when the stock closed at $48.

It's important to note that the all-in approach could outperform scaling-in if the price moves in the trader's favor immediately after entry. However, scaling in provides a way to manage risk and optimize entries in scenarios where the price moves against the trader initially.

Drawbacks to Scaling Into A Position

While scaling-in offers several benefits, it's essential to understand the potential downsides of this approach. One of the main drawbacks is the risk of missing out on profits if the price moves in the trader's favor immediately after the initial entry.

For example, if a trader scales into an extended position and the price quickly rises, they may have yet to accumulate their full desired position size before the price moves beyond their planned entry points. This can reduce overall profitability compared to going all-in at the initial entry price.

Additionally, scaling in may result in higher transaction costs, as the trader places multiple orders instead of a larger order. This can be particularly significant for traders with smaller account sizes or those trading instruments with high transaction fees. 

Lastly, the psychological aspect of scaling-in should not be overlooked. Some traders may find it challenging to stick to their scaling plan, especially if the price moves against them and they are tempted to abandon the trade before reaching their full position size. Of course, as algorithmic traders, this is less of an issue because your trading plan is executed automatically. However, you may still be tempted to intervene.

Scaling-In Is Martingale Trading And Is Dangerous!

The comparison between scaling in and martingale trading is a common misconception, and it's important to clarify the differences between the two approaches.

Martingale trading is a strategy in which a trader doubles their position size after every losing trade, with the idea that a winning trade will eventually recover all previous losses and generate a profit. This approach is hazardous and can blow up an account, as the position sizes can quickly become unmanageable. A series of consecutive losses can result in substantial drawdowns or even account ruin.

In contrast, scaling-in is a risk management technique that involves gradually entering a position in smaller increments rather than doubling down on losing trades. When scaling in, traders typically have a predefined plan for the number of increments, the size of each increment, and the price levels at which they will add to their position. The goal is to achieve a better average entry price and manage risk by limiting the impact of a single entry point, not to recover losses from previous trades.

We're all familiar with the concept that you should risk at most 2% of your trading account on any single trade. How would that look with the scale-in technique?

When using a 2% risk rule combined with scaling in, traders must ensure that their total risk across all scaled-in entries does not exceed 2% of their account balance. This requires careful planning and position sizing to maintain a consistent level of risk throughout the trading process.

Let's consider an example to illustrate how the 2% risk rule can be applied when scaling into a position:

Suppose a trader has a $50,000 account and wants to scale into a long position in Stock XYZ. The stock is currently trading at $100 per share, and the trader has determined that the stop-loss level will be placed at $95, which is 5% below the current price.

Using the 2% risk rule, the trader can risk up to $1,000 (2% of $50,000) on this trade. To calculate the maximum total position size, the trader divides the risk amount by the difference between the entry price and the stop-loss level:

Max total position size = $1,000 / ($100 - $95) = $1,000 / $5 = 200 shares

Now, the trader decides to scale into the position using three equal increments. They will buy 67 shares (rounded up from 66.67) at $100, $99, and $98, resulting in a total position size of 201 shares.

1. First entry: 67 shares at $100

2. Second entry: 67 shares at $99

3. Third entry: 67 shares at $98

If the price reaches the stop-loss level of $95, the trader will lose approximately $1,005 (201 shares * $5 loss per share), which is close to their 2% risk limit.

However, if the price moves in the trader's favor and they exit the trade at $105, for example, their profit would be $1,005 (201 shares * $5 profit per share).

In this example, the trader has effectively scaled into the position while adhering to their 2% risk rule. By dividing the total risk across multiple entries, the trader has reduced the impact of a single entry point and potentially improved their average entry price.

It's important to note that traders should also consider the impact of commissions when calculating their position sizes and risk levels. 

Does It Work? An RSI2 Example

Let's test scale-in on a simple trading system. We are using the well-known 2-period RSI strategy on the @ES.D market. We'll be moving to the micro contract to scale into our position. Remember, the micro contracts are 1/10 the size of the mini. Thus, if we buy 1 contract of the @ES market, the equivalent is 10 micro contracts. This will allow us the ability to scale into our position.

The trading rules are:

• Initial entry when RSI(2) < 25
• Sell position when RSI(2) > 75
• No Stop
• No Regime Filter
• $1.50 commission and $1.25 slippage taken out per side.
• All orders are next bar at market

Scale-In Rules are:

• Initial purchase 4 contracts upon trading entry rules
• Second purchase 3 contracts when Close falls below .1% of initial purchase price
• Third purchase 3 contracts when Close falls below 1.5% of initial purchase price

Below is an example of a couple of trades.

The total number of contracts for a full position will be 10. I initiated the purchase of 4 contracts because dividing 10 over three entries is not a viable option when working with contracts. Can't buy a fraction of a contract!

Let's look at the results of our two simulations. One performing an all-in approach vs our scale-in technqiue.

I would have to say the results are underwhelming. It's true that the Max Drawdown and Net Profit fell. That was expected, but the risk-adjusted return (NP vs DD) also fell. I would like to see this value increase, not fall.

In essence, we're generating less profit with only a marginal improvement in the drawdown. This serves as a valuable lesson in testing ideas. What may seem promising in theory, may not always yield the expected results in practice. This learning experience underscores the dynamic and unpredictable nature of trading strategies. 

Below is an example of the all-in (left side) take a trade. The scale-in (right side) is taking the same trade with three entries.

I will test the same two strategies with a 200-bar regime filter added. I'll only take trades when the price is above this simple moving average.

 By focusing our trades during a bull market, the scale-in version does what is intended. That is, we see an improvement in the risk-adjusted return. We go from a 4.7 to a 5.0 in the net profit vs. drawdown metric. It's not too impressive, but it's a positive change.

Conclusion: The Importance of Testing Scaling In Strategies

Throughout this article, we have explored scaling into positions as a technique for algorithmic traders using TradeStation and EasyLanguage. We have discussed the potential benefits, such as managing risk and optimizing entry prices. We have also provided examples of implementing scaling-in strategies using EasyLanguage code and shared tips for customizing your approach.

However, this exploration's most critical takeaway is the importance of testing your scaling-in strategies before deploying them in live trading. Using the RSI(2) strategy on the @ES.D market, the example we provided demonstrates that what may seem like a promising idea in theory may not always yield the expected results in practice.
In our initial test, we found that scaling into positions led to lower net profit and only a marginal improvement in drawdown, resulting in a decreased risk-adjusted return. This outcome highlights that not all scaling-in strategies will be effective in every market condition or with every trading system.

However, adding a simple 200-bar regime filter to focus our trades during a bull market showed a slight improvement in the risk-adjusted return. This finding underscores the importance of testing and refining your scaling-in strategy to suit your specific trading system and market conditions.

As algorithmic traders, it is crucial to approach scaling-in with a scientific mindset. Thorough backtesting and forward-testing of your strategies are essential to ensure they align with your trading objectives and risk management rules. It is also necessary to regularly review and optimize your scaling-in approach based on changes in your trading system's performance and market conditions.

Remember, the goal of scaling in is to improve your trading performance, not to add unnecessary complexity or risk to your system. By diligently testing and refining your strategies, you can determine whether scaling in is viable for your specific trading style and market conditions.

In conclusion, scaling into positions is a powerful tool for algorithmic traders using TradeStation and EasyLanguage. However, its effectiveness relies heavily on thorough testing, customization, and continuous optimization. By embracing the importance of testing and adapting your strategies to suit your unique trading environment, you can harness the potential benefits of scaling in and working towards achieving consistent, risk-adjusted returns.

The code I used for this study is available for download. See below.