👤
I don't just manage
crypto products.
I trade them too.
100,000+ app downloads. 5+ years in the markets. ACCA student. CFA is my next goal. I build crypto products from the inside — as someone who actually uses them every day.
0
App downloads led
5+ yrs
Active crypto trading
0
Live tools built
ACCA
Student · CFA next
👋 Who are you? Jump straight to what matters most to you
Scroll down and read everything — it's all here.
Featured Case Study
How I grew Chart AI to 100K+ downloads ⏱ 3 min read
⭐ Featured Project
Crypto Product Lead
● Live · 100K+ Downloads
Chart AI — Crypto Charting & Analysis App
⚠ The Problem
Retail crypto traders lacked a mobile-first charting tool combining real-time technical analysis, automated pattern recognition, and actionable market context in one place. Existing tools were desktop-heavy, expensive, or built by people who'd never traded a single candle.
✓ My Approach
As the sole in-house crypto domain expert, I bridged trader needs and engineering output — identifying underserved features, scoping them with trader-first thinking, coordinating development, and validating every release from a practitioner's lens.
My Contributions
🎯
Feature Prioritization
Scoped high-impact features from real trader pain points, eliminating low-value requests before dev.
🔍
Domain Expertise
Sole crypto expert on team. Every feature passed through my trading lens before shipping.
🎨
UX Direction
Directed UI upgrades to improve trader workflow, reduce cognitive load, and lift retention.
🤖
AI Analysis Tools
Designed automated market analysis tools — scoped logic, defined accuracy benchmarks, validated vs live markets.
📣
Content Strategy
Led crypto marketing strategy — keyword targeting, trader audience positioning, and ICP-resonant messaging.
🚀
New Product Dev
Contributing to new crypto product from ground up — discovery, research, feature architecture, roadmap.
0
Total Downloads
#1
Domain Expert on Team
11
Months Leading Product
Multi
Roadmaps Owned
Key Product Decisions
Why automated analysis over more chart types?
After analyzing trader behavior, users spent more time seeking entry signals than exploring chart layouts. Automated pattern recognition delivers repeated daily value — chart type variety is a one-time choice. Signal generation keeps users coming back.
How did I validate before committing dev resources?
I stress-tested every proposed feature against my own trading sessions first. If I couldn't see myself using it while a trade is live, it didn't make the roadmap. This eliminated speculative features and kept the sprint lean.
What was the hardest product tradeoff?
Balancing simplicity for retail vs depth for experienced traders. Solved with progressive disclosure — clean defaults with advanced layers accessible but not mandatory, keeping both cohorts engaged.
"The best crypto products are built by people who use them. I'm one of the few PMs who can validate a feature in the morning and ship a spec by afternoon — because I tested it in a live trade."
— Areeb Ali, on why domain expertise matters in crypto product
Lessons Learned
Traders need signal, not noise
Mobile-first means tap-first
Domain expertise is a product moat
Ship fast, learn fast, compound
Retention > Acquisition for trading apps
Speed of feedback matters as much as accuracy
Internship — iTechGemini
3 months · 2023
Crypto product research, feature analysis, and content development. Learned the full crypto product workflow from ideation through delivery and stakeholder communication.
Technical Background
React · Python · Full-stack
I evaluate feasibility honestly, estimate effort accurately, and communicate with engineers in their language — eliminating the translation layer that slows most PMs down.
Proof of Work
Evidence, not claims ⏱ 2 min per tab
How I actually think and work — documented.
PRD: Automated Pattern Detection Engine
Overview
ProblemTraders miss chart patterns due to screen fatigue and multi-timeframe complexity
GoalAutomate recognition of 12 high-probability chart patterns across 3 timeframes
Success Metric≥70% accuracy vs manual ID; 20%+ DAU increase within 30 days of launch
PriorityP0 — Core feature for Q1 roadmap HIGH
User Pain Points (from trading experience)
- Scanning 20+ charts manually takes 2–3 hours — patterns missed by the time user acts
- Most pattern tools use lagging indicators — signals arrive after the move
- No mobile-native solution exists — existing tools are desktop-only
- False positives erode trust faster than missed signals
Feature Scope
In ScopeHead & Shoulders, Double Top/Bottom, Bull/Bear Flags, Triangles, Wedges, Cup & Handle
Out of ScopeElliott Wave (complexity vs accuracy tradeoff), Harmonic patterns (v3 candidate)
Timeframes1H, 4H, 1D — highest signal-to-noise ratio based on 5+ yrs trading experience
Acceptance Criteria
- Pattern overlay renders within 800ms of symbol load MUST
- Confidence score displayed (Low / Medium / High) with color coding MUST
- Push notification on new pattern detection (opt-in) SHOULD
- Historical hit rate visible per pattern type NICE
Domain Rationale
- 1H/4H/1D chosen over lower TFs — retail traders are trend-followers, not scalpers
- Confidence score prevents over-reliance — trading is probabilistic, not deterministic
- Excluded harmonic patterns — too subjective for automated detection at current accuracy
🔍
Competitor Analysis
Market Gap: Crypto Charting Apps
Analyzed 8 top crypto charting apps on App Store and Play Store. Consistent gaps: no mobile-native automated analysis, no narrative context, UX designed for desktop traders forced onto mobile.
Key insight: Every major player treats mobile as a "viewer" — we treated it as a primary trading surface. That's the positioning gap.
👥
User Research
Retail Trader Pain Points
Synthesized user feedback, competitor app store reviews, and personal trading experience to identify top 5 recurring pain points: signal overload, pattern fatigue, slow loads, missing macro context, poor alert systems.
Most apps solve charting. We solved decision-making. That's a different product entirely.
| Feature | Chart AI (Us) | TradingView Mobile | Coinigy | Delta App |
|---|---|---|---|---|
| Automated Pattern Detection | ✓ Yes | ✗ No | ✗ No | ✗ No |
| Mobile-First UX | ✓ Native | ~ Adapted | ✗ Poor | ✓ Yes |
| Narrative / Macro Context | ✓ Built-in | ✗ No | ✗ No | ✗ No |
| AI Analysis Layer | ✓ Yes | ~ Premium only | ✗ No | ✗ No |
| Free Tier Value | ✓ High | ~ Limited | ✗ Paid gate | ✓ Good |
I directed UX decisions based on how traders actually behave in markets — not how designers assume they do.
Before — Original Dashboard
Flat menu with 12+ items on home screen
All indicators shown by default (clutter)
Chart loaded after 3 taps
Alert system buried in settings
No macro or news context on chart view
After — My UX Direction
Focused home: watchlist + top signal
Indicators hidden by default, toggled on demand
Chart opens from one tap on any symbol
Alert icon always visible on chart view
News + macro widget inline with chart
Before — Pattern Alerts
No pattern detection existed
Users had to scan charts manually
No confidence scoring system
After — My Product Spec
12 patterns auto-detected across timeframes
Push notification on new pattern formation
Confidence score: Low / Medium / High
How I connect macro, narrative, and technical signals — the same mental models that shape every product decision.
Narrative Research
Identifying crypto narratives before they peak
Narrative cycles follow: early adopters → influencer amplification → retail FOMO → peak → rotation. I track social velocity, developer activity, and liquidity flows to identify narratives at stage 1–2.
"The best narrative trade is one where you're already positioned when everyone else starts asking 'have you heard about X?'"
Macro Analysis
DXY, liquidity cycles & crypto correlation
Crypto doesn't trade in isolation. A weakening DXY with expanding global liquidity (M2) is historically the most reliable macro tailwind for BTC. Built Crypto Macro Intelligence because no mobile product connected these dots for retail traders.
"When central banks print, hard assets pump. Crypto is the highest-beta expression of that thesis."
Technical Framework
Why I prioritize 4H/1D over lower timeframes
Lower timeframes generate noise. 4H and daily structures capture institutional order flow — the only flow that moves markets meaningfully. This informed every pattern detection feature I specced: designed around timeframes that matter.
"Trade the chart institutions trade. Everything else is reading tea leaves."
Products Built
Tools I built because they didn't exist
Each started as a trading problem I couldn't solve with existing products.
Live
Crypto Narrative Terminal
Know what the market is chasing before the crowd does
Tracks dominant narratives driving crypto markets in real time — built on 5+ years of pattern recognition.
- Real-time narrative tracking across 10+ sectors
- Momentum scoring — identifies acceleration early
- Historical narrative cycle comparison
- Rotation alerts when smart money moves sectors
PythonReactAPIsNLP
🔒 Private — available on requestRoadmap: Sector rotation alerts
Live
Crypto News Analyzer
Signal from noise — instantly
Filters and scores crypto news by market impact. 95% of crypto news is noise — this tool finds the other 5%.
- Sentiment & impact scoring per headline (1–10)
- Macro-aware context for every story
- Source credibility weighting
- Real-time: Bullish / Bearish / Neutral tagging
PythonGPT APIRSSReact
🔒 Private — available on requestRoadmap: Telegram bot
Live
Crypto Macro Intelligence
The big picture most retail traders never see
Connects global macro events to crypto markets — DXY, Fed policy, liquidity cycles. The context professional traders have, built for retail.
- DXY, M2 liquidity & BTC correlation dashboard
- Fed meeting impact analysis
- Risk-on / Risk-off environment indicator
- Liquidity cycle position tracker
PythonFRED APIReactCharts
🔒 Private — available on requestRoadmap: Weekly macro briefing
In Development
Crypto Algo
Manual edge, systematized
Algorithmic trading system built on years of manual experience. Proven discretionary setups encoded into rules-based, emotion-free execution.
- Rules-based entry, exit & position sizing
- Backtested across 2020–2025 market cycles
- Max drawdown & daily loss limit built in
- Paper trading phase before live deployment
PythonBacktestingExchange APIPandas
📊 Backtesting phaseTarget: Live Q3 2026
How I Build
My product process
Every product follows this framework — adapted from seeing what works and what wastes time.
Discovery
Market gaps, personal trading pain
Research
Competitor analysis, user pain
Validation
Test in live markets before dev
Roadmap
Prioritize: impact × effort
Design
UX direction, trader-first flows
Develop
Specs, dev collaboration
Test
QA vs real market conditions
Launch
Content, positioning, ASO
Measure
DAU, retention, adoption
Iterate
Ship fast, compound gains
My edge in discovery
I test features in live markets before specs are written
- Trade the product's intended use case myself
- Identify what's missing vs what's nice-to-have
- Validate urgency: "would I pay for this now?"
- Document the exact pain moment — not the vague idea
My edge in prioritization
Trader impact score × engineering cost matrix
- Every feature gets an impact score based on trading frequency
- Dev effort estimated, sanity-checked by my technical background
- High-impact / low-effort ships this sprint
- Low-impact / high-effort doesn't make the roadmap
My edge in development
Technical background eliminates the translation layer
- Write specs engineers can implement without ambiguity
- Identify what's feasible vs wishful thinking
- Catch scope creep early — protect sprint velocity
- Review implementations for crypto accuracy, not just function
My edge in iteration
Market feedback loops accelerate learning
- Live market conditions expose edge cases QA sheets miss
- User behavior in volatile markets differs from calm
- Track retention by feature cohort, not just overall
- Kill features that don't compound engagement in 30 days
Research & Trading
How I think about markets ⏱ 2 min read
My trading experience shapes every product decision I make. Here's how I actually analyze markets.
Macro Framework
The Global Liquidity Cycle & Crypto
BTC's biggest moves correlate more with global M2 money supply than with halving events alone. When central banks expand balance sheets, risk assets inflate — crypto is the highest-beta expression. I track DXY, US10Y, and Fed balance sheet weekly.
Product application: Built Crypto Macro Intelligence to give retail traders the same macro context institutional desks use daily.
Narrative Analysis
Crypto Runs on Stories, Not Fundamentals
Every major crypto cycle has been driven by a dominant narrative: DeFi Summer, NFTs, L2 scaling, AI tokens, RWA tokenization. The narrative determines where liquidity flows — being early is more valuable than being right on fundamentals.
Product application: Built Narrative Terminal to surface early-stage narratives before they hit mainstream coverage.
Technical Edge
Why Higher Timeframes Win
5+ years of trading taught me 80% of profitable setups come from 4H and daily charts. Lower timeframes create illusions of opportunity but destroy risk/reward. Pattern reliability drops sharply below 4H. This directly informed every Chart AI pattern detection spec I wrote.
Product application: Chart AI's pattern detection focuses on 1H/4H/1D — the timeframes with signal, not noise.
Prop Firm Experience
Trading with Professional Risk Rules
A year of prop firm trading forced discipline I couldn't self-impose: max daily drawdown limits, position sizing rules, no revenge trading. These constraints paradoxically improved returns by eliminating emotional decisions.
Product application: Crypto Algo will enforce all prop-firm-style risk rules by default — removing the human from risk management.
5+
Years Crypto Trading
Spot, futures, DeFi — across multiple bull and bear cycles
1 yr
US Equities & Indices
Macro-driven plays, prop firm funded account trading
Multi
Market Cycles Navigated
2020 crash, 2021 bull, 2022 bear, 2024–25 cycle
ACCA
Financial Rigor
ACCA student, CFA next goal — building professional-grade financial analysis skills
Impact
Measurable results
📱
0
App Downloads Led
Chart AI, as Crypto Product Lead
📈
5+
Years Active Trading
Crypto, US Equities, Prop Firm
🛠
0
Live Tools Built
Narrative, News, Macro Intelligence
📜
ACCA
Student
CFA is the next goal
🎯
0
Months as Product Lead
iTechGemini — Karachi, PK
🔍
Solo
Crypto Domain Expert
Only trader on the product team
⚡
Multi
Market Cycles Lived
Bull, bear, DeFi, NFT, L2, AI narratives
🏦
1yr
Funded Prop Trading
Professional risk management applied
About
Why I build crypto products
My philosophy
Why crypto?
I've been in these markets long enough to know most crypto tools are built by engineers who've never traded, or traders who can't build. That gap is where I live. I understand both worlds and translate between them to build products that actually work in the markets.
What makes me different?
I test every feature I spec in live market conditions before committing dev resources. My trading account is my most honest product validation tool. If I wouldn't use it while a trade is live, it doesn't ship.
What am I building toward?
A world where retail crypto traders have the same quality of tools as institutional desks — at a price they can afford. I'm building products that close the information asymmetry between retail and institutional, one tool at a time.
The combination that's rare
₿
Crypto Trader (5+ years)
Spot, futures, DeFi — lived through every major cycle
🎯
Product Lead (iTechGemini)
Shipped features that reached 100K+ users
📜
ACCA Student + CFA Next Goal
Financial rigor anchors every product decision
💻
Technical Background
React, Python — I can build what I spec
🏦
Prop Firm Experience
Professional risk management discipline
Background
Education & Career Journey
How I got here
2019 – Present
Active Crypto Trader
Started trading crypto — spot, then futures, then DeFi. Survived the 2022 bear market. Traded through NFT, L2, and AI narrative cycles.
2023
Internship — iTechGemini
Crypto product research, feature analysis, content development. First time applying trading knowledge to a product context.
2024 – Present
Crypto Product Lead — iTechGemini
Leading Chart AI to 100K+ downloads. Sole domain expert on the team — trader, researcher, and PM in one role.
2024 – Present
Prop Firm Trading
Funded account trading with professional risk rules. Max drawdown limits, position sizing discipline, no emotional decisions.
Next goal
CFA Program
After completing ACCA — adding CFA to combine professional financial rigor with crypto market expertise.
Qualifications
📜
ACCA
Association of Chartered Certified Accountants
⏳ Currently pursuing
📊
CFA Program
Chartered Financial Analyst
🎯 Next goal after ACCA
₿
5+ Years Crypto Trading
Spot · Futures · DeFi · Prop Firm
✓ Active since 2019
🏢
Crypto Product Lead
iTechGemini · 2024 – Present
✓ 100K+ app downloads
💻
Technical Skills
React · Python · REST APIs · Data Analysis
✓ Self-taught builder
Skills & Stack
What I bring to the table
🎯 Product
₿ Crypto & Trading
💻 Technical
📜 Finance & Credentials
📜
ACCA
Association of Chartered Certified Accountants
⏳ In Progress
📊
CFA Program
Chartered Financial Analyst
🎯 Next Goal
Want to build something together?
Open to crypto product roles, trading tool collaboration, fintech opportunities, and research partnerships — remote or Karachi-based.
🎯 Ideal Role
Crypto Product Lead / Manager at a crypto startup, exchange, or fintech
📍 Location
Remote globally or on-site in Karachi, PK
⚡ What I bring
Domain expertise, trading credibility, execution speed, financial rigor
🤝 Open to
Full-time roles, consulting, product advisory, research collaboration
Usually responds within 24 hours