Updated
TL;DR — Kyle Risso’s systematic trading lessons
Kyle Risso describes building a quantitative trading business around liquidity, asset-specific risk limits and repeatable processes. His interview covers an illiquid crypto loss, manual Asian-session setups, algorithmic trading and reinvesting reported payouts in business infrastructure. These are his personal experiences, not typical trader outcomes.
One Prop Trader a Day – Episode 84
Kyle Risso
Kyle Risso is a 23-year-old systematic trader from the United States who runs a quantitative trading business, nearly lost his company on an illiquid crypto token, and rebuilt everything around liquidity, disciplined risk and repeatable processes. Here is his story, in his own words.
Is profitable trading a research problem rather than a talent problem? Kyle Risso, a 23-year-old systematic trader in the United States who runs the quantitative firm HP FX-G across forex CFDs, metals, crypto and indices, treats it as an engineering problem, and bought his first instant funding account in March 2025 to test the model. In this interview, he explains what surprised him about the payout rules, the illiquid crypto token that nearly cost him the company, and why every payout goes back into servers and infrastructure.
Kyle’s funding journey and business reinvestment
My name is Kyle Risso. I am 23 years old, based in the United States, and I run a quantitative trading business called HP FX-G. We focus on market and data analysis, algorithmic trading, automated systems and blockchain development, and we mainly trade the Forex CFD markets along with metals, crypto and the major equity indices. Trading is the core of everything we do.
For the first three years after founding the company, we traded almost entirely with our own capital. That gave us experience, but limited capital made growth slow. Prop trading looked like a way to increase the company’s earning potential, so in March 2025 I bought my first instant funding account to see how the model worked. What surprised me was that even an instant funding account still required a 10% profit before I could request a payout. I had assumed that after years of profitable trading I could simply apply the same strategies with more capital. I quickly learned that succeeding in a funded environment requires a different mindset and a different approach to risk – one of the most valuable lessons of the whole experience.
My first payouts did not go toward anything personal – they went straight back into the business. I treated every payout as company capital and reinvested it into the infrastructure that keeps us running: servers for the algorithms, VPN services, crypto on and off ramps, and the tools we use day to day. For me the real milestone was not buying something for myself. It was reaching the point where the business could start funding its own growth.
In the first eight months after that first account, I went through roughly five accounts before I finally hit the 10% target that removed the payout restrictions. Some of that came from broker costs, but most of it came from mistakes I had to learn by experience – holding through low-liquidity periods, carrying trades over weekends when the rules did not allow it, trading news without a plan, and using the same position size across assets with completely different volatility. Prop trading taught me that consistency matters far more than creativity. My edge came from building repeatable processes and treating every trade like part of a disciplined system rather than trying to outsmart the market.
Why liquidity changed Kyle’s approach
The most expensive lesson I ever learned was not really about a dollar amount – it was that I almost lost everything the company had. Before Forex CFDs, I traded highly illiquid crypto tokens on centralized exchanges while I was still in college. I started the business with about $4,000, and poor execution, high costs and emotional decisions cut that in half. Trying to recover, I put nearly everything I had left into an extremely illiquid token. I watched it collapse from around $1,500 to about $30, then the rally I had been waiting for finally came and briefly took the account back to roughly $4,000 – but when I tried to sell, there were almost no buyers. By the time liquidity returned, the price had collapsed again and I exited for a fraction of its value. That experience permanently changed how I think about markets. Liquidity is just as important as price, and I have avoided low-liquidity markets ever since.
My lowest point came right after that trade. The company still existed, but most of its capital was gone. Around then I discovered Forex CFDs, where high liquidity and flexible position sizing let me build strategies with much less capital. The transition was not smooth – my very first CFD trade lasted a few seconds because I misunderstood leverage and opened a position that was far too large. I was learning a whole new market while balancing college and finding ways to support myself, whether that meant mowing baseball fields or joining research studies. But quitting never crossed my mind. One rule I refused to break was protecting my long-term future: I never borrowed money to trade and never touched my retirement savings. Those rules still guide my risk management today.
Early on, my biggest source of stress was not losing money – it was the fear of missing something while I was not watching the market. A lot of the crypto exchanges I used did not have the risk tools active traders take for granted, so I worried about big moves while I slept. That led to one of the first things my company ever built: a monitoring system that tracked volatility and sent alerts to my phone, wired to a wearable that would wake me up if an unusually large move happened overnight. It was unconventional, but it showed how seriously I took protecting open positions. Once I moved into Forex CFDs with better liquidity and tools, that fear faded. Today the emotion that drives me is not fear, it is determination. Every setback reinforced my belief that profitable trading is ultimately an engineering problem you improve through research, testing and disciplined execution.
Manual and algorithmic trading systems
| Aspect | Manual trading | Algorithmic systems |
|---|---|---|
| Timing | Mostly Asian-session setups; usually one manual trade a day. | Systems run around the clock. |
| Decision process | Kyle rates setups on a five-star scale and requires his risk rules to be met. | Automated execution, with a notification for Kyle to review each trade. |
| Risk lesson | Define the stop before entry and keep the plan consistent. | Match the risk model and position size to the asset’s volatility. |
| Daily work | Selective entries, mainly for prop accounts. | Research, backtesting, monitoring and system improvement. |
Source: Kyle Risso’s account in the sections below. This compares his reported processes, not their profitability.
Today my manual trading follows a very structured process, while my algorithms are more flexible. Running a business means I cannot sit and watch charts all day, so I built my manual strategy around consistency instead of hunting for the perfect setup. Rather than only taking A+ trades, I rate every opportunity on a five-star scale, and anything three stars or higher is tradable as long as it meets my risk rules. When every trade follows the same process, the data I collect actually means something. Most of my manual trades happen in the Asian session, where I look for compressed volatility on liquid, low-spread markets before the European and US sessions bring higher volatility. I avoid news trading, high-spread instruments and anything I consider unpredictable. My algorithmic systems are different – they run around the clock, so I give them far more flexibility than I give myself.
On a typical day my focus is actually running the business while the systems monitor the markets. Every automated trade sends me a notification so I can review it and step in if needed, but most of my time goes into researching, backtesting and improving systems rather than staring at charts. Manual trading is reserved mostly for prop accounts, where I still like to make the decisions myself, and I usually take only one manual trade a day. I call it my wake up to money trade: I find a compressed, low-volatility setup during the Asian session that has the potential to break out during the European session while I am asleep. The stop is defined before I enter and the plan does not change. Ideally I wake up with a profitable position to manage. Not every trade works, but the process is simple, repeatable and disciplined – exactly what I look for.
Asset-specific risk controls and patient scaling
My most recent losing trade actually came from one of my algorithms, not a manual trade. A strategy built for lower-volatility Forex markets generated a trade on Bitcoin, where the volatility and exposure were much higher, and it ended in about a 4% drawdown because the risk model was not designed for that asset. Nothing unusual happened in the market – the lesson was about risk. Now, if a market needs more risk than my limits allow even at the smallest size, I simply do not take the trade. That is one variable I refuse to compromise on. It is also why I ignore the popular advice that you should only trade A+ setups. Those exist, but they are not common enough to build a business around, especially if you cannot watch the markets all day. My edge does not come from waiting for perfect conditions – it comes from applying the same process consistently and controlling variance across a large number of trades.
Trading has been received very differently depending on who I talk to. I kept it almost entirely to myself when I started at 16, and only took it seriously enough to build a business around in my third year of college, when I was struggling academically and realized I needed a practical skill that could give me a future. Not everyone supported it – my father especially saw trading as gambling and believed a traditional career was safer. I understood that, but I wanted something different: independence and a business built on my own terms. Outside my family, people have been surprisingly supportive, often fascinated by the technology behind algorithmic trading. At this stage my focus is not on maximizing personal income, it is on building capital, because trading skill alone is not enough without meaningful capital behind it. That is a big part of why the prop model interests me.
What separates me from someone who washed out is not talent or unlimited capital – by my third evaluation I was running very low on money. What kept me going was believing that successful trading is a solvable problem. I approached it almost like a mathematical proof: I knew consistency was possible because others had already done it, so the challenge was building a repeatable process that worked for me. Along the way I let emotions influence decisions, drifted from my plan, and took profits too early while letting losses run to full stop. Eventually I simplified everything – a structured process, objective data, constant refinement – and built the manual strategy I still use today. The advice I would give myself a year ago is to be far more selective about where I invest my time and money, because the industry is full of products that promise performance and do not deliver. If prop firms disappeared tomorrow I would still be trading; they accelerate growth, but they are only one part of the business, alongside brokerage partnerships and the systems I build. And if someone handed me a one million dollar account today, the first thing I would do is nothing – evaluate the account, confirm the conditions, take only small positions, and prove any system under smaller conditions before scaling it. Capital deserves patience. If someone trusts me with that account, my job is to protect it first and grow it second.
To Omer and the team, thank you for building a place where real traders can share the honest version of this journey. To anyone still grinding: treat trading like an engineering problem, protect your capital above everything, and build a process you can repeat. Consistency, not brilliance, is what compounds.
About the writer – Kyle Risso
Kyle Risso is a 23-year-old systematic trader from the United States who runs the quantitative trading business HP FX-G, combining manual and algorithmic strategies across Forex CFDs, metals, crypto and indices. After nearly losing his company on an illiquid crypto token, he rebuilt everything around liquidity, disciplined risk management and repeatable processes, and now trades funded accounts through firms including Instant Funding. He treats profitable trading as an engineering problem to be solved through research, testing and disciplined execution.
Frequently asked questions
What is Kyle Risso’s trading approach?
Kyle describes a systematic approach combining manual trades with algorithmic systems. His manual process focuses on liquid, low-spread markets, predefined stops and repeatable setups during the Asian session.
What did Kyle learn from his crypto loss?
In this interview, Kyle says an illiquid token left him unable to sell when its price recovered. His lesson was that liquidity matters alongside price, and that position sizing must fit each asset’s volatility.
Does every instant funding account require a 10% profit before a payout?
No universal rule is established by this interview. Kyle’s 10% figure describes the account conditions he encountered at the time. Check the provider’s current terms for the specific account before purchasing.
Sources and interview context
Primary source: Kyle Risso’s original first-person interview above, published in JoinProp’s One Prop Trader a Day series. His account history, losses, payouts and trading methods are self-reported experiences, not independently verified performance or industry-wide statistics. The 10% payout threshold refers to the account conditions he describes at that time, not a universal or current rule.
“Liquidity is just as important as price.” — Kyle Risso, in this interview. His LinkedIn profile provides additional professional context. The Instant Funding link in this article is a referral link; JoinProp may earn a commission.
Enjoyed this article? Add JoinProp as a preferred source on Google.
