Ben & Alex: Complete Podcast Conversation

Alex (Guest)

Thank you, Ben. It’s a real pleasure to be here especially in my hometown, Moscow. I’ve been following the Why Varianse series for some time, and it’s exciting to finally sit down in the Varianse Studio to share my story in automated trading.

I believe our audience will really enjoy seeing how algorithmic systems are transforming the markets and why I’m so passionate about combining logic, data, and technology to achieve trading discipline.

Alex (Guest)

I’d say about 30 years since my very first trade.

Alex (Guest)

Back in high school, I was already thinking about what I wanted to do with my life. Believe it or not, my first dream wasn’t finance. I thought I’d end up in sports management. Maybe a sports agent, even owning a football team one day.

But everything changed when my dad handed me a copy of Forbes magazine. Inside was the famous list of the 400 wealthiest people, and I became fascinated. I started cutting out their profiles and organizing them by industry. My plan was simple: whichever folder was thickest by the time I graduated, that’s where I’d build my career.

After a few years, the clear winner wasn’t sports or manufacturing it was finance. What struck me was that these money managers and hedge fund founders weren’t all geniuses; they were simply disciplined, curious, and willing to take calculated risks.

That was my lightbulb moment. Around that time, I was helping athletes on the side, and a mentor told me,

“You’ve got the skills for this lean into finance.” That advice gave me the push to take my first real step into trading.

Alex (Guest)

My mentor really encouraged me to learn how to manage money properly, and that got me into the financial business. From there, I became fascinated with studying some of the greatest investors and traders in history. I’d spend hours at the library digging through old articles on microfiche. Do you remember those?

Alex (Guest)

Nope. It was like this piece of plastic film you fed into a big machine that projected the text on a screen. One of the first people I really studied was Warren Buffett. His approach completely opened my eyes. At the time, I was living in Toronto, and I also came across Michael Lee Chin, who was building a reputation as Canada’s answer to Buffett.

He ran a small firm, and I worked hard to get in there. That’s where I really started to study value investing. I realized my curiosity was pulling me more toward trading itself, not just managing money through mutual funds, but really understanding the mechanics of markets and building strategies. That’s when my knowledge base started shifting toward trading full time.

Alex (Guest)

As I got deeper into the industry, I spent a lot of time with money managers, and I noticed something interesting. They’d talk about individual stocks names that had doubled, tripled, even more over time. But when you looked at the overall fund performance, it was just 10%, 12%, maybe 14%.

It made me think: if you built a more focused portfolio, or traded these individual names directly, the potential returns could be so much greater. That realization pulled me away from being satisfied with those ‘regular’ returns, and pushed me toward a more active, hands on approach to trading.

Alex (Guest)

I wanted a bigger upside than traditional investing offered, and I was inspired by traders I had read about years ago in Forest magazine. I wanted to try an active approach that could potentially achieve those kinds of results.

Alex (Guest)

After a few years of manual trading, I kept running into the same problem: even if my strategy was solid, human emotions and timing errors kept holding me back.

One night, I was having dinner with some fund managers. They casually mentioned that their systematic trading fund returned 281% that year. I was like, wait… what?

That’s when it hit me. This isn’t magic, this is rules, executed perfectly by a machine. A few months later, I got recruited by a firm building a machine driven trading platform. I became president, worked with engineers and data scientists, and saw firsthand how a well coded system could scan the markets, place trades, and manage risk all hands-free. It was like watching trading in hyperdrive.

That was my turning point. I realized I didn’t want to trade manually anymore. I wanted to build systems that trade for me, free from emotion, disciplined, and scalable. That led me to start my own Algo fund in my place, at a time when APIs were rare and mentorship was almost non-existent.

Today, my trading isn’t about staring at charts, it's about designing smart systems that work around the clock, even when I’m not watching.

That’s the beauty of rules-based trading: it's like giving your strategy a brain of its own.

Alex (Guest)

Ah, that’s a fun story. Honestly, the moment I realized trading bots trade better than humans hit me when I was just testing a few scripts. I was grabbing coffee, and the system executed trades perfectly on its own. No hesitation, no panic, just following rules. I remember thinking,

‘Wait this is actually working!’

That’s when I knew I had to go all in with algo trading

Alex (Guest)

Of course! I double checked every single trade in the beginning. But slowly, I realized the system doesn’t make emotional mistakes it doesn’t overthink.

Humans panic, algorithms don’t. That discipline! Total game changer for me.

Alex (Guest)

Initially, it was just me experimenting with Python and MQL5 scripts. Super simple rules to start: if price breaks the previous high, enter; if it falls below support, exit.

After I saw it working historically, I brought in a small team of engineers. That’s when it went from a personal side project to something scalable, something I could actually run professionally.

Alex (Guest)

The turning point? After the first year of backtesting and live trading. Consistent profits, minimal drawdowns, and the system didn’t care if I was asleep or in a meeting. That’s when I realized this wasn’t just a tool it was a business model that could scale globally and that’s exactly what I did next

Alex (Guest)

Absolutely Ben. At its core, my approach is entirely rule based. I design the logic around clear market conditions - breakouts, trend reversals, support resistance levels, volume spikes. Everything is coded so that the mechanism drives without human intervention, minimizing emotional bias.

Think of it like an automated decision tree where every ‘if then’ condition has been backtested rigorously over years of historical data.

Alex (Guest)

I believe AI is going to change things massively. The amount of information traders can access today is on another level compared to when I started. Back then, if you came across a new concept, say VWAP, you'd have to stop, Google it, dig through Investopedia, hope the explanation was accurate, then figure out how it applied to your strategy. It was time consuming and not always clear. Now?

You just open ChatGPT or any of these AI models, type “Explain VWAP like I’m five,” and instantly you get a crystal clear breakdown. That’s a game changer. It speeds up learning, it removes friction, and it allows traders whether beginners or pros to go deeper into systems, test faster, and iterate smarter. So no, it’s not hype. It’s real. The traders who learn how to integrate AI into their workflow are going to learn faster, adapt faster, and potentially become profitable faster. To me, it’s like turbocharging the education and development cycle in trading.

Alex (Guest)

Correct. Look, I’ve been using intelligent systems since the late ’90s, early 2000s. Back then, AI was already doing the heavy lifting on statistics, testing, crunching numbers, things that used to take me days or weeks could be done in minutes. Now, with the chat side of AI, it’s phenomenal.

If you’re a developer and you know trading, you can say, “Write this code,” and boom you’ve got something you can test. But here’s the catch: if you don’t know trading, if you don’t understand strategy or risk management, it’s garbage in, garbage out. That’s why I always tell people to learn the strategy first, get a mentor if you need one. AI can accelerate you, but it won’t save you from a bad foundation.

Alex (Guest)

Exactly. Backtesting is non-negotiable. I run strategies on multiple timeframes intraday, daily, and even weekly to see how they behave under different market conditions. That’s where platforms like Varianse come in. They provide me with reliable historical data, advanced charting, and real-time testing environments. It’s not just a tool, it's like having a sandbox where I can simulate trades at scale before deploying live.

The support from their team has been really helpful too; any data queries or platform issues are resolved quickly, which is critical when you’re running algo systems

Alex (Guest)

Well, let me be honest, I didn't land on Varianse right away. I went through the usual journey that most algo guys do. I tried one platform that looked great on the surface, but when I dug in, the historical data was full of gaps. Imagine spending weeks fine tuning a system, only to realize your backtests were based on incomplete price feeds. That was frustrating. Then I moved to another well known platform. The tools were fine for discretionary traders if you just wanted to click buy and sell manually, it worked.

But the moment I tried to automate, the API was clunky and limited. It would freeze during peak market hours, or miss execution windows. For algo trading, that’s a deal-breaker. By the time I got to Varianse, the difference was night and day. The data was robust, the latency was low, and the backtesting actually reflected real-world conditions. On top of that, the support was incredible. Anytime I had a technical question or needed help with an API integration, their team actually understood the problem and fixed it quickly.

For me, that was the turning point. I realized Varianse wasn’t just another platform; it was built with systematic traders in mind and that’s why I’ve stuck with it and I would say the biggest reason?

Because it provides me fast execution and low slippage. Yeah that’s really important for me.

Alex (Guest)

Exactly. Beyond platforms like Varianse, I’ve been actively contributing to the MQL5 community for several years. Sharing strategies, reviewing code snippets, and discussing optimization techniques with other strategy developers has sharpened my approach.

It’s a knowledge-sharing ecosystem I learn, I teach, and together we improve the robustness of automated systems. For anyone getting started in computational trading, participating in communities like MQL5 is invaluable.

Alex (Guest)

Sure. Let’s take a simple breakout strategy: once the algorithm detects a breakout above a defined resistance level, it triggers an entry. Stop-loss levels are calculated dynamically based on volatility, and targets are pre defined using historical ATR (Average True Range). Orders are sent to the broker API instantly. If a condition isn’t met or the price reverses, the system exits automatically. It’s fully autonomous.

Humans just supervise and refine; they don’t intervene in execution. Now, that’s the “vanilla ice cream” version. What I’m not giving away are the sprinkles on top like my tools for detecting market sentiment or filtering out false positives with a dash of AI and machine learning. Those are staying in the secret sauce… at least until maybe a future episode if Ben bribes me with enough coffee.

Alex (Guest)

Yes. Varianse lets me see live P&L, order flow, and alerts in one dashboard. Combined with my own backend scripts, I can scale strategies across multiple instruments with minimal overhead.

Alex (Guest)

Absolutely. MQL5 is where I test new ideas, benchmark strategies, and collaborate with other quant coders globally. Some of the concepts I’ve discussed there have been directly integrated into my live systems. It’s a continuous cycle of learning, coding, testing, and improving.

Alex (Guest)

That’s a big one. Overfitting is the silent killer of strategies. You can make any backtest look amazing if you tweak enough parameters, but that doesn’t mean it’ll survive real markets. What I do is stress test across multiple instruments and randomize the data. For example, shuffle candles or add noise and see if the strategy still holds up. If it breaks easily, it’s not robust enough for live trading.

Alex (Guest)

Exactly. A strategy that looks profitable on paper can collapse if you’re half a second late or constantly slipping 1 - 2 pips. That’s where Varianse made the biggest difference for me. Other platforms just couldn’t guarantee stability under high load. Varianse’s low latency infrastructure and tight spreads turned my strategies from ‘theoretically profitable’ into actually profitable.

Alex (Guest)

That’s true. Many platforms give you clean-looking charts but the historical data is full of gaps, or worse, survivorship bias. You think your strategy is bulletproof until you run it live and suddenly it falls apart. With Varianse, the historical feeds are clean and reliable. I can trust the data, which means I can trust my backtests.

Alex (Guest)

Oh, plenty! On some brokers, APIs would freeze during NFP or high volatility events. Imagine your stop-loss not triggering because the API hung for 30 seconds. That's a nightmare. With Varianse, I’ve had stability even during volatile events. When I did run into integration questions, their tech team was quick to respond. That kind of support is rare.

Alex (Guest)

Well, scaling is where infrastructure really matters. Running the same algo on 30 instruments means you need pristine market data, stable execution, and enough computational power to handle simultaneous trades.

Varianse’s environment, combined with my own automation scripts, gives me that flexibility. Without it, you’d constantly run into bottlenecks.

Alex (Guest)

Definitely. Just because the trading logic deploys, it doesn’t mean you won’t feel nervous watching drawdowns. Many traders kill good systems too early because they don’t trust the process.

The key is building robust strategies you believe in so you don’t second-guess your own code. That’s where proper testing and reliable platforms come in.

Alex (Guest)

If you’re just starting out, here’s my advice: don’t rush to automation. First, master the fundamentals, learn how markets move, learn technicals, understand execution. Trading is money, and money demands respect. Treat it like a craft, not a game.

Once you’ve built that foundation, then step into algo trading. At the end of the day, it’s just logic ‘if this, then that’ coded into a system. The beauty is, once the rules are solid, the machine will trade them with zero hesitation, zero emotion.

But remember, your system is only as good as the knowledge you put into it. Garbage in, garbage out. So be professional. Be disciplined. Keep learning, keep refining. If you do that, algo trading isn’t just a tool it’s a gateway. It can give you freedom, scalability, and a career that’s limited only by how far you’re willing to push yourself.

Start with one clear strategy, keep it simple, backtest thoroughly, and leverage platforms like Varianse for reliable data. Join communities like MQL5 to learn and share and remember, patience and discipline matter more than chasing the next ‘hot’ strategy algorithms don’t sleep, and neither should your preparation. So take it seriously, invest in your skills, and I promise this journey can change your life.