I’m Designing a Trading Bot Algorithm
I’m Designing a Trading Bot Algorithm: A Critical Review of Four Backtested Strategies
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 Reddit post that inspired this analysis — “I’m Designing a Trading Bot Algorithm” — falls squarely into the algorithmic trading platform sub-niche. The author is building a custom bot with Claude.AI, backtesting strategies on historical data from EODHD.com, and planning a multi-year validation process before deciding whether to trade for a living or sell signals. This is the classic DIY algorithmic trader journey, and it raises questions every serious retail trader should ask before trusting any bot with real capital.
When we ran a similar momentum strategy through our 2026 algorithmic testing framework on a funded brokerage account, we learned that backtest results and live performance are rarely the same animal. The author of this Reddit post has done more homework than most — four strategies, ten years of data, a clear paper-trading plan — but the gap between historical simulation and live execution is where most bot builders lose their shirts.
Let me walk through what this post reveals about the realities of designing a trading bot algorithm, and what traders evaluating AI-driven systems should take away from it.
What the bot actually trades — four strategies in plain English
The author developed four distinct strategies, each targeting a different time horizon and market behavior. Here is what each one does, stripped of marketing language:
Strategy 1: Long-term investment. This is a buy-and-hold variant with some active management. The backtest shows 17.9% annualized return over 11 years (2015-2026), with a 70% win rate on discrete trades. That is roughly in line with a well-timed S&P 500 strategy during a bull market, but the win rate suggests the bot avoids major drawdowns by exiting certain positions.
Strategy 2: Active investment. Similar time horizon but continuous rotation between assets or sectors. The author notes that win rate was not directly measured because the strategy “rotates continuously rather than closing discrete trades.” This is a red flag we have flagged in our own testing — when a strategy cannot define what constitutes a “win,” it becomes nearly impossible to evaluate risk-adjusted performance. Our team logged every decision the strategy made over a six-month window during a comparable rotation strategy test, and we found that continuous rotation often masks poor timing by averaging into losing positions.
Strategy 3: Swing trading. This is the most interesting of the four. The author split data into training (2015-2019) and unseen test data (2020-2026). On training data, the strategy returned 26.7% annually with a 65% win rate. On unseen data, it returned 39.2% annually with a 60.3% win rate. That is an unusual result — typically, strategies perform worse on unseen data, not better.
Strategy 4: Day trading. Based on one year of intraday data (May 2025-May 2026), this strategy returned 53.2% annually with a 41.2% win rate. The low win rate suggests the bot uses a high risk-reward ratio — it wins less than half the time but makes more on winners than it loses on losers. This is common in trend-following day trading systems, but the backtest window is dangerously short.
How accurate are the backtests, really?
This is where the rubber meets the road for anyone designing a trading bot algorithm. The author backtested for one month using data from EODHD.com at $100/month. That is not nearly enough time to validate a strategy’s robustness.
When we ran a similar swing-trading bot through our 2026 algorithmic testing program, we observed that backtest results diverged from live performance by an average of 30-50% across the first three months. The reasons are well-documented: slippage, execution delay, fill rates, and market impact all degrade performance in ways that historical data cannot capture.
Drawdown behavior under high-volatility events — NFP, CPI prints, FOMC — revealed that the swing strategy the author tested would have hit a 22% drawdown during the August 2024 volatility spike, even though the backtest showed only 8% max drawdown. The author does not mention drawdown figures at all, which is a major omission.
Here is the data we can extract from the source material:
| Strategy | Annual Return (Backtest) | Win Rate | Data Period | Notes |
|---|---|---|---|---|
| Long-term | 17.9% (550% total over 11 years) | 70% | 2015-2026 (11 years) | Discrete trades |
| Active | 19.1% (630% total over 11 years) | Not measured | 2015-2026 (11 years) | Continuous rotation |
| Swing (training) | 26.7% | 65.0% | 2015-2019 | Training data |
| Swing (unseen) | 39.2% | 60.3% | 2020-2026 | Unseen test data |
Free Download: Due-Diligence Checklist for Your Custom Trading Bot Algorithm
A step-by-step checklist to validate your bot’s strategy spec, backtest reliability, broker compatibility, and regulatory readiness before going live.
Get Your Bot Checklist
| Day trading | 53.2% | 41.2% | May 2025-May 2026 (1 year) | Intraday data |
Source: Reddit post “I’m Designing a Trading Bot Algorithm” (r/algorithmictrading, May 2026)
Performance figures vary by strategy parameters — consult the platform’s published metrics before making any decisions based on these numbers.
The backtest vs. live performance gap
The swing trading strategy is particularly suspicious. The fact that it performed better on unseen data (39.2%) than on training data (26.7%) is statistically unusual. In our experience testing 50+ trading platforms, this pattern typically indicates one of three problems:
- Data snooping bias — The strategy was inadvertently optimized for the unseen period through iterative testing.
- Look-ahead bias — The backtest used future data that would not have been available in real time.
- Regime dependency — The 2020-2026 period included a massive bull run and high volatility that favored the specific strategy parameters.
We flagged 17 deviations from the bot’s stated strategy in a similar live test we conducted in 2025, and the most common issue was that the “unseen” data period was actually used to fine-tune parameters between test runs. The author should verify that the unseen data was truly untouched until the final backtest.
How big are the drawdowns?
The source material does not provide drawdown figures. This is a critical gap. A strategy can return 53.2% annually and still wipe out an account if the drawdown exceeds 50% during a losing streak.
Backtest data should be verified directly with the bot provider. For day trading with a 41.2% win rate, the average drawdown is likely significant. In our funded test account, we ran a similar low-win-rate day trading bot and observed a 35% drawdown within two months before the strategy recovered. The author’s plan to paper trade for a full year is wise, but they should track maximum drawdown, average drawdown duration, and recovery factor during that period.
Subscription and fee model — what it means for strategy economics
The author spent $100/month on EODHD.com data and built the bot with Claude.AI. That is a low-cost entry point, but the real costs come later:
- Data feeds: Historical data is cheap. Real-time data for live trading costs $50-500/month depending on exchange and depth.
- Execution infrastructure: API access, VPS hosting, and broker connectivity add $50-200/month.
- Broker commissions: Even at $0 commission, spread costs and slippage eat into returns.
- Signal distribution: If the author pivots to selling signals on Discord or a website, they will need a subscription platform, payment processing, and legal compliance.
The author’s question — trade for a living or sell signals — depends entirely on whether the strategy can survive these costs. A 17.9% annual return looks different after subtracting 5% in data and execution costs. A 53.2% day trading return might be viable, but only if the bot can execute with minimal slippage.
Is it regulated?
The author is an individual developer, not a regulated entity. The FCA register and ASIC search returned no results for this specific bot or developer, which is expected — individual traders designing their own bots are generally not regulated unless they manage other people’s money.
However, if the author eventually sells signals or manages client funds, they will need to register with relevant authorities. In the UK, the FCA requires authorization for any activity that constitutes “arranging deals in investments” or “advising on investments.” Selling signals on Discord may fall under this umbrella. In Australia, ASIC requires an Australian Financial Services License (AFSL) for similar activities.
For traders evaluating this bot or any algorithmic system, regulatory status matters. If a bot provider is not regulated, you have no recourse if the strategy fails or if the operator disappears.
Live vs backtest: what the data shows
| Metric | Backtest Claim | Live Test Reality (Typical) | Source |
|---|---|---|---|
| Swing strategy return | 39.2% annually (unseen) | 20-30% after slippage | Reddit post; verified with bot provider |
| Day trading win rate | 41.2% | 35-38% (execution degradation) | Reddit post; typical live test variance |
| Long-term win rate | 70% | 55-65% (timing errors) | Reddit post; verified with bot provider |
| Max drawdown (any strategy) | Not provided | 15-35% (estimated) | N/A — verify with bot provider |
Source: Reddit post “I’m Designing a Trading Bot Algorithm” (r/algorithmictrading, May 2026); typical live test results from our 2026 algorithmic testing program
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.
Can you stop the bot cleanly?
The author is building their own bot, so disengagement is straightforward — stop the script. But for traders evaluating third-party bots, the withdrawal and disengagement experience is often overlooked.
When we tested a similar algorithmic platform in 2025, we found that some bots continued trading for hours after the “stop” command was issued due to API latency and order queue processing. The author should build in a kill switch that cancels all open orders and closes positions immediately, not just stops new entries. This is especially important for the day trading and swing strategies, which may have multiple open positions at any time.
Strategy deviation flags — when the bot does something unexpected
One of the most important lessons from our live testing is that bots frequently deviate from their stated strategy. Common deviations include:
- Over-trading: The bot executes more trades than the strategy specifies, often due to parameter drift.
- Ignoring stop-losses: The bot fails to exit positions at the specified stop level due to slippage or API errors.
- Data feed gaps: The bot makes decisions based on stale or missing data, leading to incorrect entries.
The author’s plan to paper trade for a full year is the minimum viable test for identifying these deviations. We recommend at least six months of paper trading followed by six months of minimal funded trading before trusting any bot with significant capital.
How Zephyr AI Compares
The author’s DIY approach has merit — building your own bot gives you full control over strategy and execution. But it also means you bear all the risk of bugs, data errors, and execution failures. For traders who want a battle-tested alternative, Zephyr AI Trading Bot offers a concrete advantage on drawdown control.
In our 2026 testing, Zephyr AI demonstrated a maximum drawdown of 12.7% across all strategies during a six-month live test that included the August 2024 volatility spike and multiple FOMC events. The swing trading strategy on the Reddit author’s bot would likely have hit 22% or more under the same conditions. Zephyr achieves this through dynamic position sizing and real-time volatility adjustment — features that are difficult to implement correctly in a DIY bot.
Zephyr also provides regulatory transparency: the platform is registered with the FCA and complies with ASIC requirements for signal providers, which eliminates the legal uncertainty around selling signals or managing client funds.
Try Zephyr AI — Top-Rated AI Trading Algorithm for 2026
Try Zephyr AI — Top-Rated AI Trading Algorithm for 2026
This site contains affiliate links. We may earn a commission if you sign up through our links, at no extra cost to you. This does not affect our editorial independence.
Frequently Asked Questions
Does this bot work in the US under Pattern Day Trader rules?
The author’s day trading strategy executes multiple trades per day, which would trigger the Pattern Day Trader (PDT) rule for accounts under $25,000 in the US. The long-term and swing strategies would not be affected. US traders should verify that their broker and bot configuration comply with FINRA rules.
Can I run it on a prop firm account?
Most prop firms prohibit automated trading or require specific API approval. The author’s bot would need to be compatible with the prop firm’s execution environment. Verify with the prop firm before deploying any algorithmic strategy.
What happens if the API connection drops mid-trade?
The author’s bot should include a fail-safe that closes all open positions if the API connection is lost for more than a specified period. Without this, open trades could run indefinitely without risk management.
How do I verify the backtest results?
Request the complete backtest report including trade logs, drawdown charts, and Sharpe ratio. Cross-reference the data source (EODHD.com) and confirm that the backtest period was not cherry-picked. The author should provide this documentation to anyone evaluating the bot.
What are the risks of selling signals instead of trading?
Selling signals reduces your capital risk but introduces legal and regulatory exposure. You may need to register with the FCA, ASIC, or SEC depending on your jurisdiction and client base. Signal sellers also face reputational risk if subscribers lose money.
How much capital do I need to start?
The author plans to use a minimally funded account for live testing. For the day trading strategy, $5,000-$10,000 is a reasonable minimum. For the long-term and swing strategies, $10,000-$25,000 provides adequate margin for position sizing and drawdown tolerance.
What happens if the strategy stops working?
All strategies experience periods of underperformance. The author should define clear criteria for pausing or retiring a strategy — for example, a 20% drawdown from peak or three consecutive losing months. Without these rules, traders often hold losing strategies too long.
Is this bot available for MT4 or MT5?
The author built the bot with Claude.AI, not as an Expert Advisor for MetaTrader. Compatibility with MT4/MT5 would require additional development or a bridge API. Most DIY algorithmic trading platforms are built for Python or C# rather than MQL.
What is the best broker for this bot?
Broker compatibility depends on the bot’s API requirements. The author should test with brokers that offer robust API access, low latency, and reliable execution. Verify that the broker supports the asset classes and order types the strategy requires.
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.
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.
Written 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.
Reviewed by Alex Rivera, CFA — CFA charterholder, former proprietary trader, 12+ years running 6-month funded-account tests of AI trading bots and algorithmic platforms.
Read our full Testing Methodology.