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Some research suggests that how traders manage emotions can be associated with trading outcomes. For example, one study reports that professional traders' use of an "antecedent-focused" emotion regulation strategy (reappraisal) was associated with a trading performance advantage, though this observed association does not establish causation and may not generalize to all traders or market conditions.¹
Separately, academic work on individual investors has reported that trading more frequently can be associated with worse performance outcomes, particularly after costs, though results vary and may depend on investor type, methodology, and market conditions.
Key Takeaways
- Impulse trading is often framed as a behavioral challenge in trading education content - for example, Alpaca's educational guide describes how emotions such as fear, greed, and overconfidence can contribute to impulsive decisions that deviate from well-defined strategies.²
- Some publishers claim structured tools can reduce emotional reactions, but the strength of evidence is not always shown in-source - for instance, LuxAlgo states "studies show" certain effects (e.g., reduced emotional reactions when using stop-loss orders) without clearly presenting the underlying studies in the article itself.³
- Journaling is frequently presented as a self-monitoring mechanism in trading content - Alpaca's guide explicitly discusses tracking wins/losses and associated emotions in a journal.²
Some sources also describe stress and emotion as common features of active trading environments. For example, one source claims that many active traders experience stress during periods of market volatility, though the methodology and supporting evidence are not clearly presented
Understanding Impulse Trading: Potential Triggers and Risks
Impulse trading is commonly described (in trading education materials) as trading activity driven more by emotion than by a predefined plan. For example, an Alpaca educational guide discusses "psychological traps" and includes a specific section describing fear of missing out (FOMO) as a trigger that may prompt traders to enter positions without adequate analysis or a well-defined strategy.²
Behavioral finance and trading psychology writing often discusses recurring themes such as fear/greed, overconfidence, FOMO, and "overtrading" as issues that can arise in trading activity (with varying definitions and levels of rigor depending on the source).⁴
Academic work in decision theory also describes loss aversion, the tendency for losses to weigh more heavily than gains for many people, which can influence how market outcomes are emotionally experienced.⁵
Building a Trading Plan That May Help Counter Impulsivity
Many trading-education sources frame "having a defined plan" as a way to reduce ad hoc decision-making. Alpaca's guide, for example, states that relying on logic, data, and predefined rules can reduce the impact of human biases, and it notes that without a defined plan traders may be more susceptible to emotional biases.²
From a behavioral-science perspective, evidence outside of trading specifically suggests that monitoring and recording progress can be associated with improved goal attainment (with stronger effects when outcomes are recorded or reported).⁶
Separately, process-focused mental rehearsal (sometimes called "process simulation") has been studied in non-trading contexts. One paper reports benefits to performance from thinking through the process steps of goal pursuit (as distinct from only imagining outcomes).⁷
Paper Trading as a Non-Live Testing Environment (Descriptive)
Alpaca provides a paper trading environment that allows users to place simulated trades without live capital. Alpaca documentation also notes that paper trading accounts come with $100,000 in virtual cash by default, and that users can request a different amount.⁸
Emotional Resilience: Practices Some Traders Discuss
Trading-education and trading-psychology sources frequently describe "process" and "self-monitoring" concepts, such as reviewing decisions, documenting reasoning, and tracking emotions, though these sources vary widely in rigor and specificity.
For example:
- Alpaca's guide includes a section titled "Keep a Journal" that discusses tracking outcomes and the emotions associated with them.²
- LuxAlgo's article describes journaling, written rules, and "verbal confirmation" as techniques, and it presents several numerical claims (e.g., on stress and emotional reactions) framed as "studies show," without clearly providing the studies in the article itself.³
Leveraging Algorithmic Trading: Potentially Limiting Emotional Decisions
Some trading-psychology commentary describes one commonly cited potential characteristic of automation is consistency in execution: rules can be executed with discipline and without hesitation, even though the trader's psychological decisions can still surface in system design and oversight.⁹
Alpaca's Trading API provides programmatic access for submitting and managing orders via API endpoints, and Alpaca publishes official client SDKs in multiple languages, including Python, .NET/C#, Node, and Go, for interacting with its APIs.¹⁰
At the same time, some trading-psychology commentary emphasizes that automation does not remove psychology entirely; it may shift psychological pressure to decisions such as when and how systems are used (e.g., whether to interfere with a system during adverse periods).⁹
Utilizing Order Types to Potentially Help Control Risk
Academic research indexed in PubMed Central reports an association between the use of stop-loss and limit orders as risk-management measures and a reduction in reported fear among traders.¹¹ Please note that this is in context to a specific study, which may not generalize and should not be interpreted as a guaranteed or typical outcome.
In Alpaca's order documentation, order behaviors are described in operational terms. For example:
- Limit orders can reduce slippage relative to market orders, but may not fill (or may fill only after time) depending on market conditions and available liquidity.¹²
- Stop (market) orders become market orders once elected; Alpaca also notes that a stop order does not guarantee a fill at a particular price after conversion to a market order.¹²
- Stop-limit orders convert to limit orders once the stop price is reached, and may remain active (unfilled) if price gaps past the executable level.¹²
- Trailing stop orders are described as automatically updating the stop threshold as price moves, reducing the need to repeatedly modify the stop level manually.¹²
Alpaca also documents bracket orders as a chained structure where an entry order, once filled, activates two conditional exit orders (a take-profit limit order and a stop-loss order). Alpaca notes that in extremely volatile and fast market conditions, both exit orders may fill before cancellation occurs.¹²
For opening/closing auction participation, Alpaca's documentation describes market-on-open / limit-on-open and market-on-close / limit-on-close order handling as eligible for the opening or closing auction respectively (with additional details referenced in its "Time in Force" section).¹²
The Potential Role of Data and Analytics in Decision-Making
Alpaca's Market Data materials describe real-time and historical market data coverage across stocks, options, and crypto, and present historical depth (e.g., "7+ years historical stock data").¹³
Alpaca's historical stock data documentation describes historical endpoints that can be used to retrieve historical market data, which can be relevant to research and testing workflows (the specific methodology and interpretation remain user-dependent).¹⁴
Options trading is not suitable for all investors due to its inherent high risk, which can potentially result in significant losses. Please read "Characteristics and Risks of Standardized Options" before investing in options.¹⁵
Monitoring and Review: Continuous Improvement Practices
Some educational content on impulsive trading characterizes impulsive decisions as a common challenge tied to both personal and situational factors (e.g., boredom, fatigue, difficulty maintaining discipline), and describes how these factors can contribute to deviations from a trading plan.¹⁶
Alpaca provides a paper trading environment in its documentation, which it positions as a simulated trading setup alongside its broader Trading API documentation.¹⁷
Frequently Asked Questions
How long does it typically take to develop discipline against impulse trading?
Behavioral change timelines may vary significantly from person to person. In a real-world habit formation study that tracked how quickly repeated behaviors began to feel more "automatic," Lally and colleagues reported substantial variation: the median time to reach 95% of a behavior's automaticity plateau was 66 days, with a range from 18 to 254 days.¹⁸
It is important to note that this study examined habit automaticity for everyday behaviors in a real-world setting, not trading-specific decision-making. As a result, any translation to "trading discipline" should be treated as indirect and uncertain.¹⁸
Can impulse trading ever be completely eliminated?
Complete elimination may be difficult to generalize as an expectation. In professional-trader research, Fenton-O'Creevy and colleagues report that emotion regulation (including the ability to separate emotions from decisions) has been associated with differences in trader performance in certain studies,, and they describe differences in emotion-regulation patterns between more and less successful traders.¹
Separately (and less formally), one trading-education source states that traders cannot remove emotion "completely," though it argues that awareness and discipline may reduce emotion's impact.¹⁹
How does impulse trading differ between discretionary and algorithmic traders?
One algorithmic-trading commentary explicitly argues that trading psychology applies to both discretionary traders and those running algorithmic trading systems, and states that "behind every algorithm is a trader making choices, about rules, risk, and execution."⁹
Separately, a behavioral-finance trading article lists common "emotional pitfalls" (e.g., FOMO, revenge trading, euphoria, fear) and notes that "even seasoned traders" can face these traps.¹⁹
Taken strictly as described by these sources: discretionary impulse behavior is often discussed in terms of real-time trading decisions under emotional pressure, while algorithmic impulse behavior may be discussed as arising through human decision points around system design, oversight, and intervention, because the human still makes consequential choices around the automated process.⁹
Should traders who struggle with impulse trading consider stopping entirely?
A systematic review and meta-analysis on micro-breaks (short breaks taken between tasks) found that micro-breaks can support well-being outcomes such as maintaining vigor and alleviating fatigue. For performance, the findings were more conditional: effects depended on factors like task type and break duration, with results not generalizing uniformly across all tasks.²⁰
On the trading-side framing, Zerodha Varsity describes impulsive trading as having both personal and situational reasons, noting that some people are impulsive by temperament, while even conscientious people can become impulsive under certain circumstances (with examples like boredom and "thrill" trades).¹⁶
What customers say about Alpaca: "Alpaca's Broker API helps our users invest in US markets and keep more money in their pockets."
The following testimonial is provided for illustrative purposes only, reflects the experience of a specific user, and is not representative of all users. It should not be interpreted as a guarantee of future performance or results.
References
- Thinking, Feeling and Deciding: The Influence of Emotions on the Decision Making and Performance of Traders, Journal of Organizational Behavior (via Open Research Online), November 2011.
- Emotionless Option Trading: A Guide for Algo Traders, Alpaca, March 2025.
- Trading Psychology: Overcome Emotional Bias, LuxAlgo, March 2025.
- The Psychology of Trading: 15 Key Challenges Traders Face and How to Overcome Them, Trademetria, September 2024.
- Advances in Prospect Theory: Cumulative Representation of Uncertainty, Journal of Risk and Uncertainty, October 1992.
- Does Monitoring Goal Progress Promote Goal Attainment? A Meta-Analysis of the Experimental Evidence, Psychological Bulletin, February 2016.
- From Thought to Action: Effects of Process- Versus Outcome-Based Mental Simulations on Performance, Personality and Social Psychology Bulletin, February 1999.
- Paper Trading, Alpaca Documentation.
- Trading Psychology in Algorithmic Trading, SummitAlgo, 2025.
- Working with Orders, Alpaca API Documentation.
- EEG-Based Emotion Classification in Financial Trading Using Deep Learning: Effects of Risk Control Measures, Sensors (via PubMed Central), March 2023.
- Orders at Alpaca, Alpaca API Documentation.
- Market Data, Alpaca.
- Historical Stock Data, Alpaca API Documentation.
- Characteristics and Risks of Standardized Options, The Options Clearing Corporation.
- Impulsive Trading: Possible Causes and Cures, Zerodha Varsity.
- Getting Started with Trading API, Alpaca API Documentation.
- How Are Habits Formed: Modelling Habit Formation in the Real World, European Journal of Social Psychology, October 2010.
- Behavioral Finance in Trading: How Emotions Influence Market Decisions, Gotrade, October 2025.
- "Give Me a Break!" A Systematic Review and Meta-Analysis on the Efficacy of Micro-Breaks for Increasing Well-Being and Performance, PLOS ONE, August 2022.
- Trading Psychology for Challenge Success, fortraders, June 12, 2025.
The Paper Trading API is offered by AlpacaDB, Inc. and does not require real money or permit a user to transact in real securities in the market. Providing use of the Paper Trading API is not an offer or solicitation to buy or sell securities, securities derivative or futures products of any kind, or any type of trading or investment advice, recommendation or strategy, given or in any manner endorsed by AlpacaDB, Inc. or any AlpacaDB, Inc. affiliate and the information made available through the Paper Trading API is not an offer or solicitation of any kind in any jurisdiction where AlpacaDB, Inc. or any AlpacaDB, Inc. affiliate (collectively, “Alpaca”) is not authorized to do business.
Fractional share trading allows a customer to buy and sell fractional share quantities and dollar amounts of certain securities. Fractional share trading presents unique risks and is subject to particular limitations that you should be aware of before engaging in such activity. See Alpaca Customer Agreement at https://alpaca.markets/disclosures for more details.
Options trading is not suitable for all investors due to its inherent high risk, which can potentially result in significant losses. Please read Characteristics and Risks of Standardized Options before investing in options.
Past hypothetical backtest results do not guarantee future returns, and actual results may vary from the analysis.
Cryptocurrency is highly speculative in nature, involves a high degree of risks, such as volatile market price swings, market manipulation, flash crashes, and cybersecurity risks. Cryptocurrency regulations are continuously evolving, and it is your responsibility to understand and abide by them. Cryptocurrency trading can lead to large, immediate and permanent loss of financial value. You should have appropriate knowledge and experience before engaging in cryptocurrency trading. For additional information, please click here.
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