My Journey Through the Markets: From Blowups to Breakthroughs
I started trading around 2000, right at the tail end of the dot-com bubble. Back then, I was a wide-eyed young man with a day job in sales, dipping into online brokerages with a modest €10.000 account. The markets were wild and Nasdaq swinging 10% in a day felt normal. I cut my teeth on momentum plays, scalping tech stocks during the crash, and even dabbled in options when they exploded in popularity.
Over 25 years, I’ve seen it all: the 2008 financial crisis, the 2020 COVID crash-and-rally, crypto booms and busts, meme stock frenzies, and the AI-driven surge of the mid-2020s. I’ve blown accounts and not once, but multiple times. Rebuilt from scratch with side gigs. Switched brokers so many times. Passed different challenges, but failed far more often.
If you’d asked me a decade ago why I struggled, I’d rattle off the classics:
- Wrong strategy (too many indicators, not enough price action).
- Wrong market conditions (choppy ranges killing my trends).
- Wrong execution (slippage, latency, fat-finger errors).
- Not enough screen time (missing the “feel” of sessions).
It wasn’t any of that. It was my behavior and my attitude toward the market. Starting the day in the red and chasing to “fix it.” Building solid green mornings, only to vomit it all back in one emotional New York open. Overtrading when bored during Asian sessions. Stringing together weeks of disciplined wins… then shattering it all in a single impulsive afternoon.
These weren’t blowout disasters—no YOLO all-ins on Dogecoin. Just small leaks, repeated for nearly two decades. A 1% overtrade here, revenge scalping there, FOMO into a news spike. Compounded, they turned profitable edges into breakeven slogs, or worse.
Admitting this is brutal. Even now, with gray hairs and a home office full of monitors, I don’t know if I can fully “fix” it. Human nature doesn’t vanish with experience. But at some point, I faced an uncomfortable truth: I was trying to perform like a professional trader… but I wasn’t tracking myself like one.
No proper session breakdowns. No consistent behavioral tagging. No data on giveback patterns. No clear metrics on how often I sabotaged green days. Just fuzzy memory—and memory lies. It romanticizes wins, buries losses, and invents excuses.
So, I stopped chasing the next holy-grail strategy (I’ve tested hundreds: ICT concepts, Wyckoff, order flow, machine learning algos). Instead, I built structure around myself. A proper journal. Business-level tracking (funded accounts are a business). Session recaps. Behavior tags. Data over ego.
This public journal—now expanded into this article—documents that process. Not to sell signals, flex PnL screenshots, or pretend I’ve cracked the code. I want to see what changes when behavior is tracked ruthlessly and treated like a CEO reviews a P&L. I’ll share weekly performance reviews, real behavioral mistakes, giveback stats, and adjustments.
If you’ve traded for years and feel like you’re fighting yourself more than the market, you don’t have a strategy problem. You have a structure problem. Let’s dissect mine—and yours—and see if we can fix it.
The Psychology of Trading And Why Behavior Trumps Strategy Every Time
Trading isn’t chess against the market; it’s poker against your own brain. Strategies are tools—replaceable, backtestable. Behavior is the operator, wired by evolution for survival, not spreadsheets.
Consider the data. Studies from firms like CXO Advisory show 80% of day traders lose money long-term. Prop firm stats (pre-2024 regulations) peg pass rates at 5-10%. Why? Not bad strategies—most funded traders use proven edges like supply-demand zones or EMA crossovers. It’s execution sabotage.
From my 25 years, I’ve quantified it. Pre-2020, my win rate hovered at 55% on a simple breakout strategy (5-minute charts, ES futures). Expectancy: +0.8R per trade. But realized return? Flat. Why? Behavior dragged it down.
Key leaks I tracked informally:
- Morning Recovery Trades: After a red open, 70% of my losses stemmed from “fixing” it. Psychologically, loss aversion (Kahneman’s prospect theory) kicks in—losses hurt twice as much as gains feel good. I doubled position sizes subconsciously.
- Session Givebacks: Solid European sessions (London open breakouts) erased in NY volatility. Boredom led to overtrading; adrenaline to sizing up.
- Discipline Erosion: After 10+ green days, ego swells. One bad news event (FOMC, NFP), and poof—rules out the window.
Memory failed me here. I “remembered” bad luck, not patterns. Science backs this: Confirmation bias makes us recall strategy flaws over personal errors. A 2019 study in Journal of Behavioral Finance found traders overestimate strategy efficacy by 30% due to selective recall.
Pro traders (think Paul Tudor Jones, not TikTok gurus) treat psychology like a skill. Jones journals every trade with emotional state. Mark Minervini tags “mental capital” daily. I was amateur hour.
Building Your Trading Business: Structure as the Ultimate Edge
Funded accounts? Prop challenges? They’re businesses. You wouldn’t run a coffee shop without receipts, inventory logs, or sales tracking. Yet traders wing it on vibes.
I reframed trading as Trader Inc. Metrics: ROI, drawdown, behavioral ROI (green days preserved). Tools: Edgewonk, TraderSync, custom Excel (free template below), and Notion for narratives.
Core principle: Track inputs (behavior) to optimize outputs (PnL). Data reveals blind spots memory misses.
Step 1: The Daily Checklist—Your Pre-Market Ritual
No trading without this. 15 minutes pre-open.
- Sleep/Health Log: 7+ hours? Caffeine under 400mg? Exercise? Fatigue doubles error rates (my -2% expectancy on <6 hours sleep).
- Market Bias: Review overnight action, economic calendar. Bias: Bullish/neutral/bearish. No bias? Sit out.
- Risk Capital: Max 1-2% per day. Position size calculator mandatory.
- Mental State: Scale 1-10. <7? Demo only.
- Rules Affirmation: Verbalize top 3: “No revenge. Cut losses at 1R. Max 3 trades/session.”
Example from my log (Feb 10, 2026, ES futures):
| Metric | Value | Notes |
|---|---|---|
| Sleep | 7.5 hrs | Good |
| Bias | Bullish (post-CPI dip) | Key level: 5200 |
| Risk | 1% ($1K on $100K acct) | 2 contracts |
| Mental | 8/10 | Focused |
| Affirm | Done | “Patience over action” |
This ritual cut my overtrading by 40% in test weeks.
Step 2: In-Session Tracking—Real-Time Tags
Use hotkeys or a sidebar app. Tag every trade during execution:
- Setup Quality: A (textbook), B (ok), C (stretch).
- Behavior Trigger: Boredom, Revenge, FOMO, Plan, Impulse.
- Session Phase: Pre-NY, NY Open, Lunch, PM.
- Outcome: Win/Loss, R-multiple (e.g., +1.5R).
Hotkey example (NinjaTrader): Ctrl+B = “Boredom tag.”
Post-session: 10-minute breakdown.
Real Data: My Giveback Patterns Exposed
Let’s get raw. I backtracked 6 months of 2025 trades (450 entries, $150K funded account). Pre-structure:
- Win Rate: 52%.
- Avg Win: +1.2R. Avg Loss: -0.9R.
- Expectancy: +0.4R/trade.
- But Profit Factor: 1.1 (barely profitable).
- Giveback Rate: 68% of green days eroded >50%.
Breakdown by behavior:
| Behavior | Frequency | Impact on Expectancy |
|---|---|---|
| Revenge | 22% trades | -1.2R avg |
| Boredom Overtrade | 18% | -0.7R |
| FOMO Size-Up | 12% | -0.9R |
| Plan (Good) | 48% | +1.1R |
Shocking: 40% of losses tied to just 3 behaviors. Green mornings (pre-NY) averaged +1.5% account; NY session wiped 1.2%.
Post-structure (last 30 days, as of Feb 2026):
- Trades: 80.
- Giveback Rate: 22% (down 67%).
- Expectancy: +0.9R.
- Behaviors tagged: 95% trades.
Proof: Structure works. But it’s early.
Case Study 1: The 2022 Crypto Blowup—Revenge in Action
November 2022. FTX collapse. BTC dumps 20%. My altcoin scalps crushed. Red Monday: -3%.
Old me: Revenge mode. Tuesday, I “fixed” it with 5x leverage longs on SOL. Result? -8% day. Weekly drawdown: 15%.
Tagging it now:
- Trigger: Revenge (post-red open).
- Setup: C (no confluence).
- Giveback: Full morning erased.
Lesson: Pause 30 mins after loss >1%. Stat: My revenge trades win 28% vs. 55% planned.
Case Study 2: 2020 COVID Rally—Boredom’s Silent Killer
Lockdown summer. Markets range-bound. I nailed AM breakouts but faded afternoons from boredom.
Data: 15 overtrades, all lunch/PM. Avg -0.8R. Total leak: $12K.
Fix: “3-trade cap/session.” Bored? Walk away. Cut overtrading 60%.
Advanced Tracking: Metrics That Matter
Don’t just log trades—mine data.
Key Metrics
- Giveback %: (Peak to close intraday)/Peak. Target: <20%.
- Behavioral Win Rate: Wins per trigger type.
- Discipline Score: % trades following plan. Target: 85%.
- Session P&L Heatmap:
Time | Avg P&L | Trades | Giveback %
------|---------|--------|-----------
00-08| +0.5% | 20 | 10%
08-12| +1.2% | 35 | 15%
12-16| -0.3% | 25 | 45%
16-20| -0.1% | 10 | 20%Visualize in Google Sheets. Mine showed NY open as sabotage central.
Tools Stack
- Journal: TraderSync ($29/mo)—auto-imports, tags, psych charts.
- Excel Template: Free download [link placeholder]. Columns: Date, Pair, Entry/Exit, R, Tag, Notes.
- Video Recap: Record 1-min screen review per session. AI transcribe (Otter.ai).
- Dashboard: Notion page with weekly KPI cards.
Weekly Performance Review Template
Post-Sunday:
PnL Summary
- Week: Feb 9-15, 2026.
- Gross: +2.8%. Net: +1.9%.
- DD: -0.7%.
Behavior Audit
- Revenge: 1 (cost: -0.5R). Adjustment: 1-day pause rule.
- Boredom: 0 (win!).
- Top Win: Planned GBPUSD breakout, +2.3R.
Adjustments
- Cap NY trades at 2.
- Add “tilt meter” alarm at -1%.
Share yours in comments—accountability accelerates.
Common Pitfalls and How to Avoid Them
- Journal Fatigue: Solution: 2 mins/trade. Automate imports.
- Ego Denial: Data doesn’t lie. Review monthly with a mentor.
- Strategy Blame: If expectancy >0 post-tags, strategy’s fine.
- Overcomplication: Start simple—3 tags max.
From prop firms: 90% fails lack tracking. Survivors? Obsessive loggers.
The Long Game: 25 Years In, Still Evolving
2026 markets? AI algos dominate HFT, retail via Robinhood 2.0.
But psychology? Timeless.
My goal: 20% annualized, <10% DD, via structure.
Follow for weekly updates—real mistakes, no fluff.
If you’re leaking profits to yourself, build the structure. Data will liberate you.
You’ve got the strategy. Now own the execution.
Go Deeper
Here’s a list of sources that cover key topics like Prospect Theory, day trading loss stats, prop firm pass rates, legendary traders’ methods (Minervini, Tudor Jones), journaling tools, and behavioral finance studies—directly supporting the narrative on behavior over strategy.
- Prospect Theory: An Analysis of Decision under Risk (Kahneman & Tversky, 1979) – Foundational paper on loss aversion driving trader revenge trades.
https://web.mit.edu/curhan/www/docs/Articles/15341_Readings/Behavioral_Decision_Theory/Kahneman_Tversky_1979_Prospect_theory.pdf - Prospect Theory for Online Financial Trading (PMC/NIH, 2014) – Real-world application to financial markets and risk decisions.
https://pmc.ncbi.nlm.nih.gov/articles/PMC4198126/ - Day Trading Statistics 2026: The Hard Truth – Data showing 72-80% of day traders lose money long-term.
https://www.quantifiedstrategies.com/day-trading-statistics/ - Prop Firm Pass Rates in 2025: The Truth Behind the Numbers (FunderPro) – Reveals 5-10% pass rates for FTMO-style challenges.
https://funderpro.com/blog/prop-trading-pass-rates-in-2025-what-the-data-really-shows/ - Think and Trade Like a Champion: Mark Minervini – Journaling techniques and mental capital tracking from a trading legend.
https://help.bigshort.com/en/articles/10698100-think-and-trade-like-a-champion-mark-minervini - Paul Tudor Jones Trading Philosophy – Insights on emotional control and journaling during market crashes.
https://www.daytrading.com/paul-tudor-jones-trading-philosophy - TraderSync: Trade Journal Review (LuxAlgo, 2025) – Detailed look at automated tagging and analytics for behavior tracking.
https://www.luxalgo.com/blog/tradersync-trade-journal-review/ - Prospect Theory Overview (The Decision Lab) – Accessible breakdown of biases like loss aversion in trading.
https://thedecisionlab.com/reference-guide/economics/prospect-theory - A Novel Method for Prospect Theory in Stock Markets (Phys.org, 2024) – Modern applications to investment sensitivity and decisions.
https://phys.org/news/2024-07-method-investment-decision-prospect-theory.html - Edgewonk Trading Journal Software (Official Site) – Professional tool for behavioral tagging and performance analytics.
https://edgewonk.com/