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I built an automated NSE market-analysis system for intraday and swing trading.
What began as a stock screener has grown into a system that connects market data, multi-layer analysis, risk controls, alerts, and outcome tracking.
⚡ Live intraday engine
During market hours, the system uses FYERS as its primary market-data source, with Angel One SmartAPI for fallback data access and optional live WebSocket ticks. It monitors up to 300 NSE stocks through a dynamic watchlist: a 250-stock core universe plus slots for emerging market movers.
Its multi-timeframe analysis combines EMA, VWAP, RSI, volume spikes, opening range breakouts, support and resistance, level breaks and retests, fair value gaps, liquidity sweeps, market structure, order-book signals, and market regime. It also monitors stock-specific news and uses relevant headlines as an input to scoring.
Before an alert is sent, the system checks signal strength, risk–reward, entry timing, cooldowns, sector exposure, and loss limits. An optional AI layer reviews qualifying setups and adds a risk note. Position monitoring, end-of-day reconciliation, and missed-opportunity tracking help measure what happens after detection.
📈 Daily swing engine
The swing pipeline starts with the NSE 500 universe. It filters candidates using delivery data and scans for VCP, breakouts, pullbacks, and result-driven momentum.
Its conviction score brings together technical structure, support and resistance, VWAP, sector strength, quarterly results, news and corporate events, delivery trends, bulk-deal activity, and the broader market regime. Reports include entry, stop-loss, target, and risk–reward levels. When no setup qualifies, the system sends a no-trade report.
🌅 A premarket module analyzes gaps and prepares a day-trading report. The project also includes a separate backtesting module, Telegram and email alerts, PostgreSQL-based history and outcomes.
The intraday loop has a configured 60-second polling interval; actual scan completion time depends on data availability and processing. Signal generation is automated, while trade execution is manual.
The engineering challenge I enjoyed most was building the full loop: detecting a setup, explaining why it qualifies, applying risk checks, and tracking its outcome.
#Python #FinTech #TradingTechnology #NSE #SystemDesign
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Marlowe Mobile Detailing
Solo-owner mobile car detailer, Austin, TX (serving Round Rock, Cedar Park, and Lakeway)
Marlowe Mobile Detailing was losing about 1 in 5 bookings to no-shows and running behind on nearly every job. This build replaces a vague "can you detail my car?" text with a fully scoped, weather-aware booking flow that sets an accurate time and price upfront, captures access instructions before he arrives, and automates rain-day rescheduling so he never has to chase a customer again.
He works alone and travels to the customer. Rain cancels outdoor jobs. Customers under-describe how dirty their cars are, so jobs run long. About 20% of bookings were no-shows because people forgot he was coming to them.
Home service businesses don't need more software. They need their existing processes to work together.
Think about a typical customer journey:
A homeowner needs an HVAC repair.
They call the business.
The call is missed because the technician is on-site.
They leave a voicemail.
Nobody follows up until later.
Then the customer calls another company.
This isn't a lead-generation problem.
It's a workflow problem.
AI automation can connect the entire process:
Customer enquiry → AI response → Qualification → Scheduling → CRM → Follow-up → Human handoff
For example, an AI system can:
• Respond to calls, texts, WhatsApp, or website enquiries
• Understand what service the customer needs
• Collect location and job details
• Check technician availability
• Schedule appointments
• Send confirmations and reminders
• Follow up on estimates
• Update the CRM automatically
• Escalate urgent or complex requests to the team
The important part is that AI doesn't need to replace the technician.
It handles the operational work around the technician.
That means fewer missed calls, faster responses, less administrative work, and a smoother customer experience.
And this applies across:
HVAC
Plumbing
Electrical
Cleaning
Landscaping
Pest Control
Roofing
Appliance Repair
The biggest opportunity for home service businesses isn't simply getting more enquiries.
It's building a system that makes sure every enquiry gets the right response and the right next step.
That's where AI automation can become a real business advantage.
Money Radar — See every charge. Stay in control.
Built with: Lovable (Lovable Cloud + Lovable AI)
Why I built it
I built Money Radar because money rarely disappears in one big mistake. It leaks out slowly through small recurring charges that people forget about. I wanted one calm place where anyone can see every charge before it happens, understand where their money goes and act on it.
My goal: help millions of people, whether students, families, freelancers or business owners, keep more of the money they earn.
What I Built
Radar - Track Everything
Keep all your subscriptions, bills, and renewals in one place, across any currency.
Result: Never lose track of what you're paying for.
Early Warning — Stay Ahead
Get alerts for upcoming renewals and price increases before you're charged.
Result: Avoid unexpected payments.
Insights — Understand Your Money
See spending trends, forecast future costs, identify money leaks, and explore savings opportunities.
Result: Make clearer decisions about what to keep or cancel.
AI Analyst — Ask Your Money
Ask questions about your finances and get simple answers based on your own data.
Result: Understand your money without needing financial expertise.
Shared Control — Manage Together
Create personal, family, or business workspaces with different roles and permissions.
Result: Keep shared finances organized and under control.
Proof — Keep Your Records
Create PDF and CSV reports and securely organize your receipts and financial documents.
Result: Keep your important financial records easy to access.
The impact I want to make:-
Awareness: people see their true monthly and yearly cost.
Savings: forgotten subscriptions, duplicates and price hikes get found and cut.
Confidence: no surprise renewals and no money stress.
Access: free at launch, works on any phone or laptop, supports many currencies and has an Urdu-capable AI, so it serves people beyond one country.
The problem
Millions of people lose money every month without noticing it: forgotten subscriptions, free trials that turn into paid plans, surprise yearly renewals and quiet price increases. Bills are spread across cards, currencies and apps, so nobody sees the full picture.
What Money Radar does
Money Radar is a personal and business money command center for recurring payments. It answers four questions:
What am I paying? Every subscription, bill and payment in one list, in any currency.
When am I paying? A renewals calendar and "coming up" list so no charge is a surprise.
What changed? Price history for each payment, with price increases flagged.
What can I save? Money-leak detection (duplicates, price hikes, forgotten and yearly charges) plus a what-if planner.
Key features
Overview dashboard with monthly and yearly totals, converted to your main currency at daily rates
Payments, Calendar and Alerts
Analytics: 12-month trend, 12-month forecast, and breakdowns by category and type
Savings: money leaks with their yearly cost, and what-if scenarios
Reports: statements, yearly summaries, category, upcoming and price-change reports as PDF, CSV or print
AI money analyst: a chat assistant that answers only from your own data (English and Urdu), with chats saved to your account
Receipts and documents attached to payments, stored privately
Workspaces for Personal, Family and Business, with Owner, Editor and Viewer roles and email invites
Ctrl/Cmd+K search, CSV export, account controls and a Free plan that is ready for Pro
Who it helps
Individuals, families, freelancers and small businesses who want to stop leaking money on recurring charges.
How it's structured
Everything is scoped to the active workspace, so payments, analytics, reports and the AI stay separate for each workspace.
How I built it with Lovable
I built it in three phases, entirely by chatting with Lovable:
Core: accounts, payments, calendar, alerts and the dashboard.
Intelligence: analytics, savings, reports and the AI analyst.
SaaS: workspaces and roles, receipts, currency conversion, search, plans and connecting all three phases together.
Lovable Cloud handles sign-in, the database (every table has row-level access rules), private file storage and server functions. Lovable AI runs the assistant, which uses read-only tools over the user's own data.
The "money leaks slowly" framing is spot on. What I find interesting here isn't just detecting subscriptions, but detecting the ones that quietly change. Price increases, trial conversions, annual renewals, duplicate services, etc. If Money Radar can surface those before the...