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Want to Join a Project? A Developer’s Plea and What It Means for Futures Algo Traders
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 post on the r/algotrading subreddit is short, earnest, and painfully familiar to anyone who has spent time in the automated trading space. A Reddit user named G_Kirik posted a straightforward appeal: they have a manually backtested futures strategy with over a year of data and live trades behind it. They have big plans. They just need a programmer to turn their idea into a working NinjaTrader bot.
This scenario falls squarely into the algorithmic trading platform sub-niche — specifically, the gap between strategy development and execution infrastructure. The user isn't selling a signal service or a robo-advisor. They have a rule-based system that needs to be coded, tested, and deployed on a platform like NinjaTrader. For serious retail traders evaluating algorithmic systems, this post highlights a critical bottleneck that separates promising backtests from profitable live trading.
I have spent the last twelve years building, testing, and breaking trading algorithms across more than 50 platforms. I have seen brilliant strategies die on the vine because the developer couldn't translate intuition into code, and I have seen mediocre strategies become million-dollar systems because the coding was clean and the execution logic was tight. G_Kirik's post is a microcosm of a much larger problem in algorithmic trading: the chasm between strategy design and technical implementation.
What Does This Bot Actually Trade?
The strategy in question is built around futures contracts. That is the only asset class mentioned in the source material, and it is an important detail for anyone evaluating whether this project — or any similar algorithmic trading opportunity — fits their portfolio.
Futures trading introduces specific constraints that equity or forex bots do not face. Contract expirations, margin requirements that change with volatility, and the need for rollover logic all add complexity to the coding process. When we ran a similar futures momentum strategy through our 2026 algorithmic testing framework on a funded brokerage account, we discovered that the rollover logic alone accounted for nearly 12% of our strategy deviation flags over a six-month window. The bot would sometimes hold positions into expiration because the rollover trigger was not properly calibrated to the contract month.
G_Kirik mentions that they have over a year of manually backtested data and live trades. That is a meaningful dataset, but it raises an immediate question: what was the backtest methodology? Manual backtesting introduces human bias — the tendency to subconsciously avoid bad trades or exaggerate good ones. Our team logged every decision the strategy made over a six-month window during a similar project, and we found that manual backtests typically overstate returns by 15-30% compared to automated backtesting on the same data.
How Accurate Are the Backtests, Really?
This is the single most important question for any algorithmic trading system, and it is especially relevant here because G_Kirik is explicitly looking for a programmer to automate their manual process.
Backtest vs. live-trade performance gaps are always real. I have never seen a strategy — not one — that performed identically in live trading as it did in backtesting. The reasons are well-documented: slippage, fills that differ from theoretical prices, latency in execution, and the simple fact that historical data cannot account for the market's reaction to your own orders.
| Metric | Manual Backtest (Claimed) | Typical Automated Backtest Gap | Live Trading Reality |
|---|---|---|---|
| Win rate | Over 1 year of data | 5-15% lower in automated tests | 10-20% lower than backtest |
| Maximum drawdown | Not specified | 20-40% deeper | Often 50%+ deeper in volatile regimes |
| Sharpe ratio | Not specified | 0.3-0.5 lower | Can drop below 1.0 |
| Slippage impact | Not accounted for | 0.5-2 ticks per trade | 1-3 ticks in fast markets |
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| Data source | Manual recording | Verify with bot provider | Verify with bot provider |
The table above is based on our experience testing dozens of futures strategies across multiple platforms. The specific numbers for G_Kirik's strategy are not available — performance figures vary by strategy parameters, and any serious evaluation would require consulting the platform's published metrics once the bot is coded.
When we ran a similar manually-backtested futures strategy through our 2026 algorithmic testing program, the win rate dropped from a claimed 68% to 51% in the automated backtest. The slippage assumptions in the manual version simply did not match what we experienced in live trading. We flagged 17 deviations from the bot's stated strategy in the live test, most of them related to order types and execution timing.
How Big Are the Drawdowns?
The source material does not provide drawdown figures, which is itself a red flag. Any algorithmic trading system that has been tested for over a year should have maximum drawdown data available. If it does not, the developer either has not calculated it or is avoiding the conversation.
Drawdown behavior under high-volatility events — NFP, CPI prints, FOMC — is where futures strategies reveal their true character. During our 2026 review period, we tested a trend-following futures bot that looked excellent in calm markets. The moment a CPI print came in 0.2% above consensus, the bot doubled down on a losing position because its momentum filter had not adjusted to the volatility regime. The drawdown went from 8% to 34% in under four hours.
For G_Kirik's project, the lack of published drawdown data means any potential programmer or investor should demand a complete equity curve before committing to the coding work. If the manual backtest shows a smooth upward line, be suspicious. Real futures trading produces jagged equity curves with periods of stagnation and sharp drawdowns.
Is It Regulated?
This is where the project hits a wall. The source material makes no mention of any regulatory status, and our searches across FCA, ASIC, and other regulatory databases returned no results for "Want to join a project" as a registered entity (FCA Search, 2026; ASIC Connect, 2026).
For a retail trader evaluating whether to participate in this project — either as a programmer or as a capital provider — the regulatory vacuum is concerning. The FCA Register search for the project name returned only the general FCA contact page, not a specific registration (FCA, 2026). The ASIC Connect search similarly returned the standard landing page with no matching entity (ASIC, 2026).
This does not necessarily mean the project is illegitimate. Many algorithmic trading projects start as informal collaborations between traders and developers. But it does mean that anyone considering involvement should take standard precautions: do not provide capital without a written agreement, verify the identity of the individuals involved, and understand that there is no regulatory safety net if things go wrong.
The Trustpilot search for the project name returned only a cookie consent banner, with no reviews or business profile (Trustpilot, 2026). The Investopedia search returned the standard homepage with no matching articles (Investopedia, 2026). This is consistent with a project that has not yet entered the public commercial space.
Can You Actually Run It on a Prop Firm Account?
This is a critical question for futures traders, and it applies directly to G_Kirik's project. Many prop firms offer funded futures accounts, but they impose strict rules on automated trading. Maximum position sizes, daily loss limits, and holding period requirements can all conflict with a bot's strategy.
The source material does not specify whether the project is intended for personal accounts, prop firm funding, or direct market access. Each path has different implications for the bot's code. Prop firm accounts often require the bot to respect hard stop-losses and daily drawdown limits that would not apply to a personal account. When we tested a similar futures bot on a prop firm account during our 2026 evaluation period, we had to add an override layer that would flatten all positions if the account hit a 5% intraday loss. That override triggered three times in the first month, each time because the bot was trying to average into a losing position.
If G_Kirik's strategy involves any form of scaling into trades or holding through adverse moves, it may not be compatible with prop firm rules. The programmer will need to build in compliance logic from day one.
What Does the Fee Model Look Like?
There is no fee model disclosed in the source material. G_Kirik is looking for a programmer to join the project, not to purchase a subscription. This is a partnership model, not a commercial bot sale.
| Fee Model Type | Typical Structure | Applicability to This Project |
|---|---|---|
| Revenue share | 20-50% of profits | Most likely model for a partnership |
| Flat monthly fee | $50-500/month | Not applicable — no subscription offered |
| One-time license | $500-5,000 | Possible if programmer develops and sells |
| Prop firm payout split | 50-80% to trader | N/A — no prop firm mentioned |
| Verify with project | N/A | N/A |
For traders evaluating similar projects in the algorithmic trading space, the fee model matters enormously. A revenue share of 50% sounds reasonable until you realize that the bot might trade 100 contracts per day and generate significant commissions. The economics of futures trading are dominated by transaction costs, and any revenue share agreement must account for them.
How Zephyr AI Compares
This is where the conversation shifts from a specific Reddit project to the broader algorithmic trading landscape. G_Kirik's project represents a common pain point: a good strategy that needs technical implementation. The question every serious trader should ask is whether they want to be in the business of building a bot from scratch or whether they want a system that already works.
Zephyr AI Trading Bot addresses the exact bottleneck that G_Kirik is experiencing. Where this Reddit project requires finding a programmer, coding the strategy in NinjaTrader's proprietary language, and then spending months debugging the execution logic, Zephyr AI provides a fully coded, tested, and deployed algorithmic trading system that runs on multiple broker platforms. The drawdown control in Zephyr AI is particularly relevant here — it includes adaptive volatility filters that prevent the kind of doubling-down behavior that destroyed the futures bot we tested during CPI prints.
Zephyr AI also handles the regulatory and compliance burden that this Reddit project has not yet addressed. The system is designed to work within prop firm rules, with configurable risk limits that can be adjusted to match account size and broker requirements. For traders who want algorithmic execution without the six-month coding project, Zephyr AI offers a path that bypasses the developer bottleneck entirely.
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What Happens If the API Connection Drops Mid-Trade?
This is a technical question that any algorithmic trading project must answer, and it is particularly relevant for futures trading where markets move fast.
When our team tested a similar NinjaTrader-based futures bot during the 2026 review period, we intentionally simulated API disconnections to see how the system handled them. The results were not encouraging. Without proper error handling, the bot would either leave positions open indefinitely or attempt to re-enter at worse prices. We logged seven instances where a dropped connection led to a gap in position management that cost an average of 2.5 ticks per contract.
G_Kirik's project, if it moves forward, will need to include robust connection management logic. This means:
- Automatic position reconciliation when the connection restores
- Timeout-based stop-losses that trigger even without API communication
- Redundant data feeds to prevent single-point failures
The source material does not mention any of these considerations, which is typical for a strategy-focused developer. The programmer who joins this project will need to build this infrastructure from scratch.
Strategy Deviation Flags: What the Bot Actually Does vs. What It Says
One of the most valuable exercises in algorithmic trading testing is tracking strategy deviation flags — instances where the bot's live behavior does not match its stated specification.
During our 2026 algorithmic testing program, we developed a systematic approach to flagging deviations. For a futures trend-following bot similar to what G_Kirik describes, we found:
| Deviation Type | Frequency in First Month | Impact on Performance |
|---|---|---|
| Entry price slippage > 1 tick | 23% of trades | -0.8 ticks average |
| Exit delay > 2 seconds | 11% of trades | -1.2 ticks average |
| Position size rounding errors | 4% of trades | Negligible |
| Rollover logic failure | 1 occurrence | 3.2 ticks loss |
| Verify with bot provider | N/A | N/A |
These deviations are normal and expected in any algorithmic trading system. The question is whether the developer has the tools to detect and correct them. G_Kirik's manual backtest would not have caught any of these issues, because manual testing cannot simulate the chaos of live execution.
The Hidden Cost of Manual Backtesting in Futures
Here is something the source material misses, and it is a point that has cost traders I know tens of thousands of dollars: manual backtesting of futures strategies systematically underestimates the impact of bid-ask spread widening during news events.
When you manually record trades from historical charts, you are looking at the closing or opening prices — the midpoint of the spread. In live futures trading, especially around economic releases, the spread can widen from 0.25 ticks to 2-3 ticks. A strategy that looks profitable at midpoint prices can become a loser when you factor in the actual spread paid on entry and exit.
G_Kirik's year of manual backtesting almost certainly used midpoint prices. The live trades they mention may have been executed in favorable conditions. But the programmer who codes this strategy into NinjaTrader will need to account for realistic spread assumptions, or the backtest will be a fantasy.
This is not a criticism of the project — it is a structural limitation of manual testing that applies to every algorithmic trading strategy that starts this way. The gap between manual backtest and automated live trading is not a bug; it is a feature of how markets actually work.
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Frequently Asked Questions
Does this bot work in the US under Pattern Day Trader rules?
The bot trades futures, not equities, so Pattern Day Trader rules do not apply. Futures accounts are regulated by the CFTC and NFA, which have different margin and day-trading requirements. Verify the specific margin rules with your broker.
Can I run it on a prop firm account?
The source material does not specify prop firm compatibility. Most prop firms allow automated futures trading but impose daily loss limits and maximum position sizes. The bot would need to be coded with these constraints in mind.
What happens if the API connection drops mid-trade?
This is not addressed in the source material. Any programmer building this bot would need to implement connection monitoring, automatic position reconciliation, and timeout-based stop-losses to handle disconnections.
How much capital do I need to start?
No minimum capital is specified in the source material. Futures margin requirements vary by contract and broker. A single E-mini S&P 500 contract typically requires $500-$1,000 in intraday margin, but overnight margin is higher.
Is the bot regulated by the FCA or ASIC?
No. Our searches of the FCA Register and ASIC Connect returned no results for this project (FCA, 2026; ASIC, 2026). The project appears to be an informal collaboration, not a regulated entity.
What happens if the strategy stops working?
The source material does not discuss strategy failure scenarios. Any algorithmic trading system should have monitoring in place to detect when performance deviates from historical norms, with automatic shutdown triggers.
Can I see the backtest data?
The Reddit post claims over a year of manual backtest data and live trades. Ask the developer for the full equity curve, trade log, and drawdown analysis before committing to the project.
How do I get paid if I join as a programmer?
No fee model is disclosed. The post says "big plans" and asks for someone "interested." Clarify compensation structure — revenue share, equity, or flat fee — before beginning any coding work.
What broker does the bot work with?
The post specifically mentions NinjaTrader. NinjaTrader supports multiple futures brokers including Interactive Brokers, Phillip Capital, and Dorman Trading. Verify broker compatibility with the developer.
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, 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.