Market Research · Data · Development

Vikram Partap

Independent research analyst delivering data-driven intelligence across global markets combined with Python-based analytics, data generation, and design solutions.

Market Research Data Generation Python Development Quantitative Analysis Graphic Design Web Development
About

The Analyst Behind
the Intelligence

VP
6+
Markets
Curiosity
100%
Independent

Vikram Partap is an independent research analyst with deep expertise across global markets. He delivers institutional-grade market data and research to private clients and subscribers alike.

His approach combines fundamental macroeconomic analysis with quantitative modelling — including Python-based data models and automation tools — to uncover insights that conventional analysis overlooks.

Beyond research, Vikram builds custom Python tools and data pipelines, and designs websites and visual assets — bridging analytical rigour with practical software and design execution.

Fundamental Analysis
Data Analysis
Python / Quantitative
Software Development
Graphic & Web Design
Data Generation
Macro Research
Client Reporting
Services

What I
Deliver

01
Market Research Reports
Comprehensive published reports covering macro trends, sector deep-dives, and price action analysis across global markets. Delivered periodically to subscribers.
02
Bespoke Client Research
Tailored, confidential research commissioned by private clients. Custom scope, custom cadence — intelligence built around your specific market thesis or mandate.
03
Python Development
Custom Python tools for data collection, analysis, and automation — built for clients who need reliable, well-documented, and maintainable code.
04
Data Generation & Analytics
Structured datasets, statistical modelling, and analytical pipelines — turning raw market data into clean, usable, decision-ready information.
05
Quantitative Modelling
Statistical and data-driven models built in Python to identify patterns, test hypotheses, and validate research findings — all within a simulated research environment.
06
Graphic & Web Design
Clean, responsive websites and visual assets — from brand-aligned design systems to fully built, interactive single-page sites.
Insights

Research &
Publications

Finance
Indian Banking Sector: Post-Rate Cycle Opportunities
Stocks · Q1 2025 · 28 pages
Sector-wide analysis of listed Indian banks as the RBI rate cycle matures — NIM trends, credit quality, and valuation re-rating potential.
Request →
Data & Analytics
Commodity Demand Forecasting: A Data-Driven Model
Data Science · Q1 2026 · 18 pages
A statistical modelling report examining commodity demand signals through structured data generation, historical pattern analysis, and Python-based forecasting tools.
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Commodities
Crude Oil: OPEC+ Dynamics & the Energy Transition Trade-Off
Request Full Report →
Quant

Quantitative
Research

Every model starts as a hypothesis and ends as a dataset. Python-based quantitative research — built, stress-tested, and iterated entirely within a simulated environment before any conclusion is drawn or reported.

100%
Simulated Environment Testing
15+
Models Built & Validated
1000s
Data Points Processed
6+
Markets Researched
Simulated Environment Testing
Every model is built and iterated inside a sandboxed, simulated environment — using historical and synthetic datasets, never live capital — so ideas can be broken, refined, and rebuilt without real-world consequence.
Simulated Environment Performance
Performance is tracked purely as research output — statistical accuracy, stability, and consistency within the simulation — giving a clear read on a model’s behaviour before it ever informs a report.
Statistical Validation
Correlation analysis, distribution testing, and walk-forward checks using pandas, NumPy, and SciPy — ensuring every finding holds up before it reaches a client or a report.
All figures above reflect research activity within a simulated / sandboxed data environment and do not represent real investment performance, or financial advice.
import pandas as pd import numpy as np from scipy import stats class DataResearchEngine: def __init__(self, source_path): self.source_path = source_path def clean_dataset(self, df): df = df.dropna().reset_index(drop=True) df['volatility'] = df['close'].pct_change().rolling(14).std() return df def correlation_matrix(self, df, columns): return df[columns].corr(method='pearson') def generate_report_stats(self, df): summary = { 'mean_return': df['close'].pct_change().mean(), 'volatility': df['close'].pct_change().std(), 'skew': stats.skew(df['close'].pct_change().dropna()) } return summary
Python & Development

Data &
Development

data_research_engine.py
import pandas as pd
import numpy as np

class DataResearchEngine:
  def __init__(self, source_path):
    self.source_path = source_path

  def clean_dataset(self, df):
    df = df.dropna().reset_index(drop=True)
    df['volatility'] = df['close']
      .pct_change().rolling(14).std()
    return df

  def correlation_matrix(self, df, cols):
    return df[cols].corr(method='pearson')

# Output: cleaned dataset + summary stats
Python Automation
Custom Python scripts and tools built from scratch — data cleaning, pipeline automation, and report generation across different datasets.
Quantitative Research
Statistical analysis, factor modelling, and data research using pandas, NumPy, and SciPy. Research-grade code translated into clear analytical reports.
Data Generation & Structuring
Building clean, structured datasets from raw market information — organised for downstream analysis, reporting, and client delivery.
Graphic & Web Design
Responsive websites, dashboards, and visual assets designed and built end-to-end — from concept through to a polished, live site.
Contact

Start a
Conversation

Whether you’re a private client, institutional subscriber, or business needing custom software or design work — I’m available for bespoke research mandates, data projects, and development partnerships.

Availability
Open to new mandates