Arrow Algo: Building an Accessible, AI-Driven Algorithmic Trading Platform for Retail Traders

I was searching for a platform that allowed me to build automated crypto trading strategies without coding or relying on black-box signals. Everything I found was either too technical and difficult to understand and operate, particularly if you are not a software developer, or too simple and not powerful enough to really generate any profitable trading strategies. No platform I found could provide a proper backtesting engine without needing to supply my own data; and even then, they could not properly simulate leveraged trading with realistic liquidation modeling. Around that time, I met my co-founder Ali, who had built an early block-based strategy builder. We immediately saw the opportunity to evolve it into a more powerful, AI-driven platform with a backtesting engine. So, instead of waiting for a better solution to appear, we decided to build the product we wished existed, transparent, flexible, and accessible. Powerful enough to give great results, simple enough that anyone could learn how to use it.

Early on, we spent money on Google Ads promoting YouTube videos before we really understood SEO or had clearly defined our niche. We were amplifying noise instead of clarity, and it showed. Most of the ads we tried to place were blocked for policy violations, and we could not even work out which policies we were in breach of or what to change. When we did get an ad approved, we were paying for views that came with no engagement, no follow-up, no click-throughs to our website, and zero chance of vitality. At this point, we joined one of the few start-up programs that we are members of. This one in particular was run by OVH Cloud, where we were given the tools to up-skill on SEO and marketing. What we learned helped us to turn this around. What really worked was the opposite approach, focused, educational content aimed directly at algorithmic traders, day traders, and crypto enthusiasts. Once we began publishing strategy breakdowns, real backtests, and practical insights, engagement improved significantly. We had found our niche rather than paying for content to be put in front of people who were not interested. Organic growth compounded because the message finally matched the audience.

It’s less about active complaints and more about early disengagement. A significant portion of new users arrive with the expectation that algorithmic trading is push-button passive income. Even with AI assistance, there is still a learning curve - understanding risk, leverage, position sizing, and strategy logic matters. When reality meets expectation, some users simply stop logging in, and retention can be low. Rather than ignoring that, we treat it as a product responsibility. We monitor user journeys closely and proactively reach out to inactive users to gather honest feedback. We’ve introduced AI-generated starter strategies to give new users early wins, improved onboarding flows, and refined educational content to better set expectations from day one. We’ve learned that retention in this market isn’t just about features - it’s about psychology. Education, transparency, and expectation management are just as critical as execution speed or exchange integrations. We are always adaptive and agile, and are currently in development of a new user dashboard and login journey, which we hope to release in Q2.

Most people misunderstand algorithmic trading in one of two extreme ways. One side believes it’s effortless passive income, click a button, turn on a bot, and watch money appear. The other side believes it’s reserved for quantitative hedge funds, mathematicians, and developers with years of coding experience. Both views miss the reality. Algorithmic trading isn’t magic, and it isn’t exclusive. It’s structured, rule-based decision-making. The real edge doesn’t come from secret indicators or hidden signals. It comes from discipline, testing, risk management, and iteration. Markets are probabilistic environments, no strategy wins forever, and no automation removes risk. What most people get wrong is thinking the tool creates the edge. The tool simply enables structure. The edge comes from understanding volatility, position sizing, drawdowns, and adapting to market conditions. The biggest failure point in this market isn’t bad technology, it’s unrealistic expectations. Traders either over-leverage chasing fast returns or quit when they encounter their first drawdown. The long-term winners treat algorithmic trading like building a system, not chasing a shortcut.

The next 6–12 months are about scaling intelligently rather than chasing vanity metrics. We’re investing heavily in expanding our AI strategy agent so it doesn’t just generate strategies, but helps users refine, stress-test, and optimise them based on market conditions. The goal is to move from simple automation to intelligent assistance, where the AI becomes a collaborative layer rather than just a builder. Retention is another major focus. We’re refining onboarding to reduce early drop-off, improving educational pathways, and creating clearer progression for users as they move from beginner to more advanced strategy development. On the infrastructure side, we’re strengthening our backtesting and optimisation engine, expanding exchange integrations, and improving performance visibility across live and demo environments. Community is also key. Strong ecosystems create stronger products. Over the next year, we’re focused on building a more engaged, educated user base, because informed traders stay longer and build better systems. The mission remains the same, make professional-grade algorithmic trading accessible, transparent, and sustainable for serious retail traders. We are still pre-revenue, and currently users can do everything they would ever need to on the free version of the app. As we grow, we may put some features behind a paywall where users need to pay to upgrade to use them. But at the moment, we are literally giving the software away while we look to build a community of traders for collaborative success.