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Building things that work

SURESH
BALARAMAN

Product thinking. Builder depth.

Real systems that ship.

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CAPABILITIES

What I Build

From strategic product thinking to hands-on engineering.
I ship systems that scale, not just MVPs.

Product Management

End-to-end product strategy, roadmapping, PRDs, and stakeholder alignment. From 0-to-1 launches to scaling existing products.

StrategyRoadmapsPRDsUser Research

Algorithmic Trading Systems

Custom trading bots, signal engines, and execution platforms. Multi-broker integration with real-time risk management.

PythonZerodha APIMT5Options

AI/LLM Integration

Enterprise-safe AI implementations. Intent parsing, workflow automation, and privacy-first GenAI solutions.

GeminiLangChainRAGPrompt Engineering

System Architecture

High-performance, scalable system design. Microservices, event-driven patterns, and low-latency data pipelines.

AsyncIORedisWebSocketsREST

Full-Stack Development

Modern web applications from concept to deployment. React/Next.js frontends with Python/FastAPI backends.

Next.jsReactFastAPITypeScript

Automation & Tooling

Custom internal tools that 10x productivity. Workflow automation, data pipelines, and operational dashboards.

StreamlitSeleniumAPIsScripting

Data Engineering

Real-time analytics, market data processing, and business intelligence pipelines that scale.

PandasSQLETLVisualization

Technical Advisory

Architecture reviews, tech stack decisions, and hands-on guidance for engineering teams.

Code ReviewMentorshipBest Practices

Have a project in mind? Let's discuss.

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System Active • 2024

STRATUM ALPHA

Institutional-grade quantitative validation engine.
Python • NumPy • Joblib • Plotly

01
CASE STUDY
01

The Problem

Why standard tools fail

⚠️

Overfitting is the Enemy

Most traders find a strategy that worked last week and bet money on it. This is 'Curve Fitting'. I needed a system that could rigorously stress-test strategies across years of data to find true statistical robustness, not just luck.

🐢

Compute Constraints

Validating a single strategy requires checking 10,000+ parameter combinations. On standard tools (like TradingView), this is impossible. Python loops are too slow. I needed high-performance compute architecture at home.

02

Architecture

High-Performance Compute

DECISION 01

Vectorization over Iteration

NumPy / Pandas

Replaced standard Python loops with vectorized array operations. Utilized CPU SIMD instructions to process entire datasets simultaneously.

RESULT: 500x Speed Increase
DECISION 02

Parallel Execution

Joblib Multiprocessing

Forked the validation process across all 16 CPU cores. Optimization is an 'Embarrassingly Parallel' problem, allowing for brute-force search of the parameter space.

RESULT: 100% Hardware Utilization
DECISION 03

3D Robustness Mapping

Plotly Search Space

Instead of finding the 'single best setting', the engine generates 3D heatmaps to identify 'Plateaus of Stability'—regions where the strategy survives parameter drift.

RESULT: Visual Validation

SYSTEM_VISUALIZATION.PY

OPTIMIZATION_COMPLETE

10,000 COMBINATIONS SCANNED

500x
Faster Compute
16
Cores Utilized
10k+
Simulations
3
Crises Averted

REFLECTION

"The engine saved me from deploying three strategies that looked profitable on paper but failed the Walk-Forward test. It proved that Validation > Optimization."

Deployed • v1.6

AI STRATEGY COPILOT

Zero-to-One FinTech UX.
Conversational Interface for Options Execution.

02
CASE STUDY
THE CONTEXT

Finance isn't creative writing.

Options trading is incredibly powerful but operationally painful. Constructing a multi-leg strategy requires finding 4 specific strike prices, calculating Deltas, and placing 4 separate orders.

Junior traders understood the theory ("I'm Bullish") but couldn't execute fast enough to stay safe.

THE SOLUTION

Intent-to-Template Architecture.

We don't let the AI do math. LLMs are bad at math.
Instead, we use a Hybrid Architecture:

Intent Recognition

Google Gemini parses vague user intent ('Safe bullish bet') into structured JSON tags.

Deterministic Math

A Python engine takes the JSON and scans the live Option Chain to find the mathematically optimal strikes.

Multi-Account Execution

One voice command fires orders to 10+ connected Zerodha accounts simultaneously.

THE IMPACT
<10s
Execution Time
(was 4 mins)
0%
Fat Finger Errors
Deterministic Safety

The Archive

Enterprise AI

Enterprise PlanGuard

PII-sanitized AI middleware that coaches engineers on Change Requests, reducing manual review time by 80%.

Python • LangChain • ServiceNowVIEW →
High Frequency

Algo Execution Platform

Centralized orchestration engine managing 12 concurrent strategies with <50ms tick-to-trade latency.

Next.js • AsyncIO • RedisVIEW →
FinTech Ops

Finance Command Center

Local-first personal finance system with automated net-worth tracking and push-notification billing alerts.

FastAPI • Streamlit • TelegramVIEW →
Algo Trading

Agentic Grid Bot

Resilient trading bot treating Google Sheets as a "Source of Truth" database for collaborative state management.

Python • Google Sheets APIVIEW →
Integration

Signal Bridge

Middleware connector bridging TradingView Webhook alerts to MT5 desktop execution with auto-risk calculation.

Flask • Ngrok • MetaTrader 5VIEW →
Internal Tools

Team Productivity OS

Zero-cost resource management dashboard with strict RBAC, replacing complex Jira workflows for agencies.

Next.js • Google Sheets BackendVIEW →
INITIATE_COLLABORATION

Ready to build systems
that scale?

Currently open for new engineering challenges.
Let's discuss architecture, strategy, or just geek out over tech.

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