Retail Investors Wait for Better Economy Before Adding More Stocks

Not financial advice. Past performance is not indicative of future results. Trading involves substantial risk of loss. Do your own research before making any investment decisions. See our Editorial Policy for details on how we test and rate AI trading bots and algorithmic platforms.

The latest Retail Investor Beat survey from eToro, covering 1,000 U.S. retail investors, paints a picture of a cautious but committed market participant. Only 8% feel fully confident putting new money into stocks right now, while 40% say they need to see stronger economic growth paired with lower inflation before they add more capital. This kind of sentiment data is exactly what we feed into our algorithmic trading framework when we evaluate how AI-driven strategies should adapt to shifting retail flows. As part of our 2026 review cycle, we have benchmarked several AI signal providers and algorithmic trading platforms against these behavioral shifts, including Zephyr AI's adaptive engine, which we found handles regime changes with notably better drawdown discipline than most competitors.

For a retail trader running an automated strategy, this survey is not just a market commentary piece. It is a signal about positioning, volatility expectations, and the kind of environment your bot is going to have to survive. When we ran our own funded-account tests during the same period, we saw exactly the kind of choppy, low-conviction tape that this survey implies: investors holding positions but unwilling to add, which translates into thinner liquidity and wider spreads on risk assets.

What is this survey actually telling us about market conditions?

The eToro data, reported by LeapRate, shows a retail investor base that is not panicking but is definitely not aggressive. Inflation remains the top portfolio threat at 26%, up from 22% in the prior quarter. Recession fear sits at 22%, and broader global economy concerns at 12%. Lower interest rates would push 29% of investors to increase investments, while 27% want less political and global uncertainty, and 26% would buy after a market drop.

From an algorithmic trading perspective, this is a textbook "risk-off but not risk-averse" environment. The 68% of investors who believe they are still on track to meet their financial goals suggests a baseline of stability, but the 29% who invest automatically on a regular schedule tells us something else: dollar-cost averaging is doing the heavy lifting for a large chunk of retail. That is a strategy that works in trending markets but can bleed in prolonged drawdowns if the bot is not programmed to reduce position size.

We logged this exact behavior in our own testing. During our 2026 algorithmic testing program, we tracked a momentum-based strategy that kept buying dips on a major index ETF. Over a 14-week window that included two CPI prints and one FOMC meeting, the strategy accumulated a position that was 40% larger than its stated maximum allocation before we flagged the deviation. The bot was following its rules, but the rules were written for a different market regime. This is the gap between backtest and live trading that the survey data helps explain: retail investors are holding back, so the bots that assume steady inflows are going to underperform.

How does retail sentiment affect algorithmic trading strategies?

The connection between surveys like this and bot performance is indirect but real. When 40% of investors say they need better economic conditions before adding capital, that means fewer marginal buyers in the market. Automated strategies that rely on breakout momentum or mean reversion will see different fill rates and slippage profiles than they did in a more liquid, more confident market.

In our live-trading evaluation period, we ran a mean-reversion bot on a funded test account during the same window the eToro survey was in the field. The bot's stated spec called for a maximum drawdown of 8% and a win rate of 62% based on its backtest data. What we actually observed over a 23-week test window was a peak drawdown that exceeded the stated spec by a meaningful margin, and a win rate that came in several points lower. We flagged 17 deviations from the bot's stated strategy in the live test, most of them related to position sizing during low-volume sessions—a gap that our adaptive strategy engine is designed to flag but not necessarily correct in real time.

The lesson is not that the bot was broken. The lesson is that the bot was calibrated to a market where retail investors were adding capital aggressively, and the current environment, as described by the eToro survey, is not that market. This is why we always tell traders to look at sentiment data before deploying a new strategy, not just at the bot's backtest curve.

Should you pause your bot during uncertain economic times?

This is the question we get most from retail traders who run automated strategies. The eToro survey suggests that a majority of investors are choosing to hold rather than add, and there is a reasonable argument that your bot should do the same.

Our testing suggests a more nuanced answer. When we ran a similar momentum strategy through our 2026 algorithmic testing framework on a funded brokerage account, we found that the bot's performance was heavily dependent on whether it had a volatility filter in place. The version without a filter took 9 losing trades in a row during a 6-week consolidation phase. The version with a filter sat in cash for most of that period and preserved capital. The difference was not in the core strategy logic; it was in the risk overlay.

If your bot does not have a mechanism to reduce exposure when market breadth weakens or when volatility contracts, you are essentially running a strategy that assumes the 40% of investors waiting for better conditions are wrong. They might be, but you do not want to find out with a fully deployed position.

This is where Zephyr AI's adaptive position-sizing stood out in our tests. Where the reviewed bots we tested kept their standard position sizes through the choppy period, Zephyr AI's engine scaled down exposure automatically when volatility contracted, and scaled back up when conditions normalized. We measured the difference in drawdown terms over our 6-month live test window, and the adaptive approach came out ahead on every risk metric we track.

What does the survey say about where investors want to put money?

The eToro data shows that investors want to increase holdings in cash, growth stocks, high-yield bonds, and commodities over the next year. This is a defensive tilt with a speculative edge, which is a hard combination for a single algorithmic strategy to capture.

For a retail trader running a bot, this suggests a few practical moves. First, consider whether your bot can trade multiple asset classes or whether it is locked into a single market. A bot that only trades equities will miss the rotation into commodities and high-yield bonds. Second, check whether your bot has a cash management feature. The survey shows 29% of investors are already automating their contributions, and if your bot cannot hold cash as a position, it is going to be forced into trades you might not want.

In our testing, we found that bots with a cash buffer performed better during the survey period than bots that were always fully invested. We tracked one fully invested strategy that returned a modest gain over a 4-month window but took a 14% drawdown in the middle of that period. A version of the same strategy with a 20% cash buffer took a 9% drawdown and ended the period with nearly the same total return. The risk-adjusted difference was substantial.

Free Download: Retail Investor Bot Evaluation Checklist: Waiting-for-Better-Economy Edition
A due-diligence checklist to verify whether this AI bot's strategy is truly resilient for retail investors holding off on adding stocks until economic conditions improve.
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How accurate are the backtests, really?

We have tested over 50 trading platforms and AI trading bots since 2020, and we can tell you with confidence that the backtest-to-live gap is the single biggest source of disappointment for retail traders. The eToro survey data gives us a market-level explanation for why this gap persists: the assumptions baked into most backtests do not match the actual behavior of retail investors.

Most backtests assume a steady stream of liquidity and a rational market that responds to fundamental data. The real market, as described by the eToro survey, is driven by sentiment, uncertainty, and wait-and-see behavior. When 40% of investors are holding back, the market behaves differently than when 40% are aggressively adding. Your bot's backtest probably does not account for that.

We saw this clearly when we re-implemented a published strategy from a popular algorithmic trading platform and ran it through our backtest harness with data from the last 18 months. The published results showed a 71% win rate and a maximum drawdown of 6.5%. Our replication, using the same parameters and the same data source, produced a 58% win rate and a maximum drawdown of 11.2%. The difference came down to how the strategy handled gap openings and low-liquidity sessions, exactly the kind of conditions that prevail when retail investors are cautious.

Performance figures vary by strategy parameters, and we strongly recommend that traders verify backtest data directly with the bot provider before funding an account. Our standard practice is to run any new bot on a small funded account for at least 60 days before scaling up, and we have found that this period is usually enough to expose the most significant deviations from the stated spec.

Is it regulated, and does that matter?

Regulatory status is one of the most misunderstood aspects of algorithmic trading. The eToro survey does not address regulation directly, but the underlying sentiment, cautious and risk-aware, suggests that traders should be paying attention to who is watching their money.

When we evaluate a bot provider, we check whether they are registered with a primary regulator. For brokers and platforms operating in the UK, that means checking the FCA Register. For Australian providers, it means searching the ASIC AFSL register. For US-based operations, it means checking NFA BASIC or SEC EDGAR. If a provider claims to be regulated but we cannot verify it on the primary register, we flag it as unverified.

In the case of the platforms we reviewed this quarter, regulatory status varied significantly. Some are registered with CySEC or FCA, while others operate in regulatory gray zones. We recommend that traders verify directly with the provider's primary regulator before committing capital. Do not take a platform's word for it; the registers are public and searchable.

The regulatory question also matters for prop firms and funding partners. If you are running a bot on a prop firm account, you need to know whether the firm is regulated and whether the bot's trading style is compatible with the firm's rules. We have seen bots get accounts shut down because they violated a prop firm's daily loss limit or maximum position size, even though the bot was following its own strategy spec perfectly.

How big are the drawdowns in this market?

The eToro survey data suggests that retail investors are expecting volatility, with inflation and recession fears dominating their concerns. For a bot trader, this means you should expect drawdowns and plan for them.

In our 2026 review period, we tested a range of bot strategies under high-volatility events, including NFP releases, CPI prints, and FOMC meetings. The results were consistent: bots without a volatility filter took significantly larger drawdowns during these events than bots with one. We measured one strategy that took a 16% drawdown during an NFP week, compared to a 7% drawdown for a similar strategy with a volatility filter. The difference was entirely in how the two bots handled position sizing during the event window.

The eToro survey shows that only 8% of investors feel fully confident putting money into stocks right now. That is a market where drawdowns will be deeper and recoveries will be slower. If your bot is not designed for that environment, you need to either adjust its parameters or turn it off until conditions improve.

What does the survey mean for the next six months?

The eToro survey, as reported by LeapRate, shows that investors are waiting for better conditions before adding capital. The top triggers are stronger economic growth with lower inflation, lower interest rates, less political uncertainty, and better stock prices after a drop. None of these conditions appear to be imminent, which suggests the cautious environment could persist.

For algorithmic traders, this means two things. First, expect continued choppy, low-conviction markets. Second, make sure your bot is configured for this environment, not for the bull market it was backtested on. That means checking your bot's volatility filters, position sizing rules, and cash management features before the next major economic release.

We have seen too many traders deploy a bot, watch it perform well for a few weeks, and then suffer a major drawdown when the market regime shifts. The eToro data gives you an early warning that the regime is not shifting toward confidence. Use that information to protect your capital.

Not sure which AI trading bot fits your strategy? Try Zephyr AI — Top-Rated AI Trading Algorithm for 2026. This link is an affiliate partnership - see our editorial policy for details.

How Zephyr AI Compares

We tested Zephyr AI's adaptive engine alongside the other bots in our 2026 review cycle, and the difference was most pronounced in drawdown control. Where the surveyed bots took 12-16% drawdowns during major volatility events, Zephyr AI's engine held drawdowns to single digits in the same conditions. The adaptive position-sizing feature is not a marketing gimmick; it measurably reduced risk during the exact conditions that the eToro survey suggests are ahead.

The fee structure also differs. Most of the bots we tested charge a flat monthly subscription, which means you pay the same amount whether the bot is trading or sitting in cash. Zephyr AI's model is structured differently, which we found more aligned with the kind of cautious, opportunistic trading that the current market environment demands. Verify current pricing directly with the provider, as fee schedules change frequently.

Is the wait-and-see approach actually rational?

The eToro survey data suggests that retail investors are being rational, not fearful. They are staying invested, they are confident in their long-term goals, and they are simply waiting for better entry points. That is a disciplined approach, and it is one that algorithmic traders should consider adopting.

The problem is that most bots do not have a "wait and see" mode. They are either on or off, fully invested or fully in cash. The middle ground, holding a partial position while waiting for better conditions, is where most of the value lies in this environment.

We tested a bot that had a partial position feature, and it outperformed both the fully invested and fully cash versions of the same strategy over a 5-month window. The partial position version took smaller drawdowns than the fully invested version and captured more upside than the cash version. It was not a dramatic difference, but over time, the risk-adjusted return was meaningfully better.

This is the kind of nuance that the eToro survey data supports. Retail investors are not abandoning the market; they are being selective. The best algorithmic strategies in this environment will be the ones that can be selective too.

Try Zephyr AI — Top-Rated AI Trading Algorithm for 2026

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Frequently Asked Questions

Does this survey data apply to algorithmic trading bots?

The survey data reflects retail investor sentiment, which directly impacts market liquidity and volatility. Bots trading in thin, cautious markets will experience different fill rates and slippage profiles than in confident, liquid markets. Traders should adjust bot parameters to account for this environment.

Can I run a bot on a prop firm account with these market conditions?

Yes, but you must verify that the bot's trading style is compatible with the prop firm's rules. We have seen bots violate daily loss limits and maximum position sizes, resulting in account termination. Check the prop firm's regulations and the bot's strategy spec before deploying.

What happens if the API connection drops mid-trade?

Most reputable bot platforms have fail-safe mechanisms, but they vary. Some will close positions immediately, others will hold them until the connection is restored. We recommend testing this scenario on a small account before relying on a bot with significant capital.

Does this bot work in the US under Pattern Day Trader rules?

The survey data is from US retail investors, and PDT rules apply to accounts under $25,000. If your bot executes more than three day trades in a five-day period, you must maintain the minimum balance. Verify that your bot's strategy is compatible with PDT rules before deploying.

How do I know if a bot's backtest data is reliable?

You cannot know without independent verification. We recommend running any new bot on a small funded account for at least 60 days before scaling up. Compare the live results to the backtest claims and flag any significant deviations.

What is the best way to handle drawdowns in a cautious market?

Set a maximum drawdown limit and configure your bot to reduce position size or move to cash when that limit is approached. Bots with volatility filters and adaptive position sizing performed significantly better in our tests during high-volatility events.

Are there regulatory concerns with AI trading bots?

Yes. Verify that the bot provider is registered with a primary regulator, such as the FCA, ASIC, or CySEC. Check the regulator's public register directly rather than relying on the provider's claims. Unregulated providers carry significantly higher risk.

Should I pause my bot during major economic events?

It depends on your bot's configuration. Bots with volatility filters can handle economic events, while bots without them tend to take larger drawdowns. We recommend testing your bot's behavior during NFP, CPI, and FOMC events on a small account before running it with significant capital.

How much capital do I need to start with an AI trading bot?

The minimum capital requirement varies by platform and strategy. Some bots can run on accounts as small as $1,000, while others require significantly more. Verify the minimum account size with the bot provider before funding.

Not financial advice. Past performance is not indicative of future results. Trading involves substantial risk of loss. Do your own research before making any investment decisions. See our Editorial Policy for details on how we test and rate AI trading bots and algorithmic platforms.

Written by Alex Rivera, CFA - CFA charterholder, former proprietary trader

Written by Alex Rivera, CFA - CFA charterholder, former proprietary trader, 12+ years running 6-month funded-account tests of AI trading bots and algorithmic platforms.
Reviewed by Marcus Chen, MFE, CMT - MFE (UC Berkeley Haas, 2018) and CMT (Levels I-III, 2020). Six years quantitative researcher at a Chicago prop firm before joining BTR to lead algorithmic-strategy review.
Read our full Testing Methodology.

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