Abhishek Agarwal | Quantitative Analyst

QUANT_TRADER_01

SYSTEM_INFO

ABHISHEK AGARWAL

QUANTITATIVE ANALYST

LOCATION: New Delhi, India

EXPERIENCE: 8+ Years

SPECIALIZATION: Algorithmic Trading

CLEARANCE: Series 7, 63, CFA

STATUS: AVAILABLE

AUM_MANAGED: $500,000,000

AVG_ANNUAL_RETURN: +23.5%

SHARPE_RATIO: 2.34

MAX_DRAWDOWN: -8.3%

WIN_RATE: 67%

PERFORMANCE_MONITOR

PORTFOLIO PERFORMANCE (%) - LAST 12 MONTHS
     40 |                                    
     35 |              ╭─╮                   
     30 |             ╱  ╰╮      ╭─╮        
     25 |   ╭────╮   ╱    ╰─╮   ╱  ╰╮       
     20 |  ╱      ╰─╯       ╰─╮╱    ╰╮      
     15 | ╱                   ╰      ╰─╮    
     10 |╱                             ╰────
      5 |                                   
      0 |___________________________________
        JAN  FEB  MAR  APR  MAY  JUN  JUL  AUG  SEP  OCT  NOV  DEC

TECHNICAL_STACK

[LANGUAGES]

> Python 3.9

> C++ 17

> R 4.0

> SQL

> KDB+/Q

[FRAMEWORKS]

> TensorFlow

> PyTorch

> Pandas

> NumPy

> Scikit-learn

[PLATFORMS]

> Bloomberg

> Reuters

> FIX Protocol

> AWS

> Docker

[STRATEGIES]

> Stat Arb

> Market Making

> Mean Reversion

> Momentum

> Options Greeks

PROJECT_REPOSITORY

[PROJECT_001] NEURAL_ALPHA_ENGINESTATUS: DEPLOYED

Deep learning system for alpha signal generation using LSTM networks.
Processing 50TB+ market data daily across 500 liquid equities.

SHARPE: 2.8HIT_RATE: 68%LATENCY: <10ms

[PROJECT_002] HFT_INFRASTRUCTURESTATUS: ACTIVE

Ultra-low latency trading system built in C++ with FPGA acceleration.
Handles 1M+ orders/second with sub-microsecond execution.

THROUGHPUT: 1M+ ops/secLATENCY: <1μsUPTIME: 99.99%

[PROJECT_003] RISK_ANALYTICS_PLATFORMSTATUS: DEPLOYED

Real-time portfolio risk monitoring with VaR, CVaR, and stress testing.
Monitors $2B+ AUM across multiple strategies and asset classes.

AUM: $2B+METRICS: 50+REFRESH: 100ms

[PROJECT_004] SENTIMENT_ANALYSIS_BOTSTATUS: TESTING

NLP pipeline using BERT for real-time news sentiment analysis.
Processes 1M+ articles daily from 100+ sources.

ACCURACY: 71%VOLUME: 1M+ dailySOURCES: 100+

PUBLICATIONS

YEAR	TITLE	VENUE	CITATIONS
2024	Machine Learning in High-Frequency Trading	J. Financial Markets	127
2023	Optimal Execution in Fragmented Markets	Quantitative Finance	89
2023	Deep RL for Portfolio Optimization	IEEE Computational Finance	156
2022	Microstructure Noise in Crypto Markets	J. Alternative Investments	203
2022	Statistical Arbitrage Using Alternative Data	Risk Magazine	178

CONTACT_INTERFACE

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EMAIL: abhi@abhi.uno

LOCATION: New Delhi, India

TIMEZONE: IST (UTC+5:30)