Ivan Pichugin Systematic Trader
ivanpichugin@outlook.com · LinkedIn ↗
About

Developing and running systematic trading systems across US Equities, Futures, Options, and Crypto. Started with discretionary execution — yet always rule-based, always structured. Full systematic was the natural next step. My focus: strategies that work across market phases, built on price mechanics, volume, and repeatable edge. Full cycle — from research and backtesting to implementation and live execution.

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Developing and running systematic trading systems across US Equities, Futures, Options, and Crypto. Started with discretionary execution — yet always rule-based, always structured. Full systematic was the natural next step.

Systematic Strategies Validated · Pending Live Deployment
In Development

Strategies that already exist in concept or early form — pending further testing, live validation, or full implementation in code.

Trading history. ↗
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2026-06-17 For years, I traded manually. I had rules, protocols, strategies — everything was defined. But execution was still on me. At some point I realized something uncomfortable: my strategies were already systematic. Every condition, every variable — written down. I was just the one pressing buttons. The problem wasn't discipline. It was leverage.
2026-06-12 Building a strategy is not about finding the "right idea". It's about having a process that turns any idea into something testable. Fast. My approach is simple: get to a working base as quickly as possible. Not perfect. Not optimized. Just functional enough to see if the core logic holds. Perfection doesn't exist — and waiting for it is just another way to avoid shipping.
About

Developing and running systematic trading systems across US Equities, Futures, Options, and Crypto. Started with discretionary execution — yet always rule-based, always structured. Full systematic was the natural next step. My focus: strategies that work across market phases, built on price mechanics, volume, and repeatable edge. Full cycle — from research and backtesting to implementation and live execution.

Every strategy I build starts with two questions: why should this work mechanically, and can I recognize and reproduce this situation consistently.

I work primarily with mid-frequency and swing timeframes — trend-following, mean-reversion momentum, and inplay setups. Each approach has its phase and context; the job is knowing which regime you're in. Signal architecture is always top-down: higher timeframes define structure and levels of interest, lower timeframes define entry.

Risk is fixed at 1R per trade — no exceptions. Every strategy is evaluated through one lens: expected value, reward-to-risk, and probability. If that equation doesn't hold, the strategy doesn't exist.

2026-06-17

For years, I traded manually. I had rules, protocols, strategies — everything was defined. But execution was still on me.

At some point I realized something uncomfortable: my strategies were already systematic. Every condition, every variable — written down. I was just the one pressing buttons.

The problem wasn't discipline. It was leverage.

When you execute manually across multiple strategies, you hit a ceiling fast — not because of skill, but because of physics. You can only watch so many things at once. I missed good signals not because I didn't see them coming, but because I physically couldn't be everywhere.

But the bigger shift was mental. When you're in execution mode all day — processing signals, managing positions, making real-time decisions — you lose the ability to observe. You're too close to the process to actually manage it.

Moving to systems changed that. I stopped thinking about individual trades and started thinking about the architecture that produces them. The analysis happens after the session. The decisions are made before it starts.

The difference in output isn't 2x. It's closer to 200x — not in returns, but in what you can actually cover, monitor, and improve simultaneously.

Trading History
Pre-systematic · normalized to 1R
Long trades · 2024 – 2025 · 238 trades
Total R
+79.8R
Win rate
31.5%
Profit factor
1.80
Avg win
+2.40R
Avg loss
−0.70R
Win / loss ratio
3.40
Expectancy
+0.34R
238 trades · Jan 2024 – Dec 2025 · Longs only · fixed 1R sizing
Methodology note

Across this trading history, position sizing was a persistent weakness — share count varied without a fixed risk framework, often while simultaneously developing and refining new strategic approaches. This inconsistency distorted raw P&L as a performance metric.

To isolate strategy edge from execution variance, all trades have been normalized to a fixed 1R unit. The charts below reflect actual entries, exits and trade outcomes — only position size has been standardized.

This analysis directly informed the systematic frameworks I subsequently built: fixed sizing rules, defined risk per trade, and structured drawdown limits.

Short trades · 2024 – 2025 · 122 trades
Total R
+1.1R
Win rate
32.8%
Profit factor
1.02
Avg win
+1.65R
Avg loss
−0.83R
Win / loss ratio
1.98
Expectancy
+0.01R
122 trades · Jan 2024 – Dec 2025 · Shorts only · fixed 1R sizing
Short trades · context note

The subdued aggregate performance of short trades reflects deliberate experimentation across multiple strategy variations — not an absence of edge.

Throughout this period, short setups were actively used as a testing ground: different entry triggers, varying exit logic, competing hypotheses running in parallel. Viewed as a blended pool, the equity curve understates the underlying edge. Isolated by strategy variant, individual short approaches show considerably stronger growth profiles.

Deprecated · single-asset · 12 trades · removed from live
Strategy retired

One single-asset short strategy ran briefly in live conditions and failed to demonstrate edge outside of the testing sample. Twelve trades in, the equity curve showed consistent deterioration with no recoverable structure — the loss pattern indicated a fundamental mismatch between the setup logic and live market conditions, not execution noise.

The strategy was pulled from trading and returned to research. The core idea remains valid as a hypothesis — but the entry definition needs to be rebuilt from a cleaner sample before it re-enters rotation.