PROGRAM ZERO 18-month live AI, LLM & full-stack programme · ₹5,999 for all 18 months · Starts 9 January 2027
Explore Program Zero →
Careers Ninza — business and startup leadership training
JOIN ZERO NINZA KIDS
JOIN ZERO NINZA KIDS
Careers Ninza
FINTECH LONG-TERM PROGRAM

FinTech & Algorithmic Trading Course

Markets, models and the risk management behind them.

DURATION
6 months
FEE
₹49,999
PAYMENT
₹10,000 × 6
FORMAT
Live · online
✓ Live mentor-led classes✓ Lifetime recordings✓ Certificate + placement support
FinTech & Algorithmic Trading course training in India — Careers Ninza, Kolkata
QUICK ANSWERS

FinTech & Algorithmic Trading course fee: ₹49,999 all inclusive, or No-Cost EMI of ₹10,000 × 6 with zero interest.

Duration: 6 months, part-time, with live weekday evening sessions from 7pm IST and weekend workshops.

Format: Live online across India, plus classroom batches in Kolkata, Asansol and Durgapur. Every session recorded and yours for life.

Who it suits: Beginner to job-ready. No prior experience required.

You finish with: A backtested strategy with honest metrics, a verifiable certificate, and twelve months of placement support.

Roles it prepares you for: Quantitative analyst, Algo trading associate, FinTech product analyst, Risk analyst.

About the FinTech & Algorithmic Trading course

Six months across two connected worlds: the FinTech stack that moves money, and the quantitative side that trades it. Instruments, strategy design, backtesting, execution and risk — taught with code you can read and reuse.

Ends with a strategy you have designed, backtested honestly, and sized against a written risk budget.

Who this program is for

If two or more of these describe you, this is the right course.

— Traders who want to move from screens to systems
— Finance and banking professionals moving into FinTech
— Engineers and analysts entering quantitative roles
— Founders building payments, lending or wealth products
— Serious retail participants who want method over tips

What you will learn

1 Read instruments, market microstructure and order types
2 Design a strategy from a testable hypothesis
3 Backtest without fooling yourself — costs, slippage, overfitting
4 Size positions against a defined risk budget
5 Automate execution through broker APIs
6 Understand the FinTech stack: payments, lending, KYC, regulation

FinTech & Algorithmic Trading syllabus

11 modules, each taught live and reviewed by a mentor.

1 Module 1 — Indian financial markets and instruments View
•Market structure: NSE, BSE, MCX, clearing corporations, depositories and the settlement cycle
•Regulatory framework: SEBI, RBI, and the rules that constrain algorithmic trading in India
•Equity markets: cash segment, delivery versus intraday, circuit limits, corporate actions
•Derivatives: futures and options mechanics, contract specifications, expiry, margining under SPAN
•Options in depth: intrinsic and time value, moneyness, the Greeks, and how each behaves near expiry
•Currency and commodity derivatives available to Indian participants
•Market microstructure: order book, bid-ask spread, market depth, impact cost, liquidity measurement
•Order types: market, limit, stop-loss, bracket, cover, iceberg, and their execution behaviour
•Transaction costs in India: brokerage, STT, exchange charges, GST, stamp duty, and why they decide strategy viability
2 Module 2 — Python for quantitative finance View
•Python environment for finance: pandas, NumPy, SciPy, statsmodels, matplotlib
•Time series data handling: resampling, alignment, timezone and trading-calendar awareness
•Vectorised computation for indicator and signal generation at scale
•Data structures for OHLCV, tick and order book data
•Working with financial APIs and broker SDKs
•Performance: avoiding loops, memory management on large tick datasets
•Reproducible research structure: notebooks for exploration, modules for anything reused
3 Module 3 — Market data: acquisition and quality View
•Data sources for India: broker APIs, exchange feeds, vendor data, free sources and their limitations
•Historical data quality problems: survivorship bias, look-ahead bias, adjustment for splits and bonuses
•Corporate action adjustment and building a clean adjusted price series
•Tick, minute and daily data: storage formats, compression, retrieval speed
•Building a local market data store with Parquet or a time-series database
•Data validation: gap detection, outlier identification, stale price handling
•Alternative data in outline and the practical difficulty of using it well
4 Module 4 — Statistics and financial mathematics View
•Returns: simple versus log returns, compounding, annualisation conventions
•Distribution of returns: fat tails, skew, kurtosis, and why normality assumptions fail
•Volatility: historical, EWMA, GARCH, and implied volatility from option prices
•Correlation, covariance and their instability in stressed markets
•Stationarity, cointegration and the Augmented Dickey-Fuller and Johansen tests
•Hypothesis testing applied to strategy returns and the multiple testing problem
•Portfolio mathematics: mean-variance framework, efficient frontier, and its practical limitations
•Risk-adjusted metrics: Sharpe, Sortino, Calmar, information ratio, maximum drawdown
5 Module 5 — Strategy design and signal construction View
•From hypothesis to strategy: stating an economic rationale before writing code
•Trend following: moving average systems, breakout, momentum ranking, ATR-based filters
•Mean reversion: Bollinger and z-score systems, pairs trading, statistical arbitrage
•Momentum and cross-sectional ranking strategies on Indian equities
•Options strategies: covered calls, spreads, straddles, strangles, iron condors, calendar spreads
•Volatility trading and the practical realities of Indian expiry behaviour
•Event-driven strategies: earnings, index rebalancing, corporate actions
•Feature engineering for financial machine learning and the leakage traps specific to time series
•Machine learning applied to markets: where it helps, where it overfits, and honest expectations
•Combining signals and avoiding the correlation blindness that concentrates risk
6 Module 6 — Backtesting without deceiving yourself View
•Backtest architecture: event-driven versus vectorised, and the accuracy trade-off
•Look-ahead bias, survivorship bias, data snooping and selection bias — each with a worked example
•Realistic cost modelling: brokerage, taxes, slippage, impact cost, and their effect on apparent edge
•Fill assumptions and simulating partial fills and rejections
•In-sample versus out-of-sample, walk-forward analysis and purged cross-validation
•Parameter sensitivity: robustness surfaces rather than a single optimal setting
•Overfitting detection: degrees of freedom, deflated Sharpe ratio, and the number of trials problem
•Monte Carlo and bootstrap resampling of the equity curve
•Performance reporting: equity curve, drawdown profile, trade distribution, exposure, turnover
•Building a reusable backtesting framework rather than one-off scripts
7 Module 7 — Risk management and position sizing View
•Risk budgeting: defining maximum loss per trade, per day and per drawdown before trading
•Position sizing: fixed fractional, volatility-adjusted, ATR-based, and Kelly with its practical caveats
•Stop-loss design and the trade-off between whipsaw and tail protection
•Portfolio-level risk: correlation clustering, sector concentration, factor exposure
•Value at Risk and Expected Shortfall, and their known failures
•Leverage and margin management under Indian rules, including peak margin requirements
•Drawdown response protocol: reducing size, pausing, and the psychology of resuming
•Scenario and stress testing against historical crisis periods
8 Module 8 — Execution and automation View
•Broker API integration in India: Zerodha Kite, Angel One SmartAPI, Upstox, Dhan and their differences
•Authentication, session management, rate limits and reconnection handling
•Order management system design: order lifecycle, state machine, partial fills, cancellations, rejections
•Live data handling: websocket streaming, tick processing, latency awareness
•Execution algorithms in outline: TWAP, VWAP, iceberg and reducing market impact
•Paper trading and forward testing before committing capital
•Deployment: server or cloud hosting, uptime, failover, time synchronisation
•Monitoring and kill switches: automatic shutdown on error rate, loss limit or connectivity failure
•Reconciliation: matching system state against broker positions every session
•Logging and audit trail sufficient to reconstruct any trading day
9 Module 9 — Regulation, compliance and the FinTech landscape View
•SEBI algorithmic trading regulations and current obligations for retail and institutional participants
•Broker-approved strategies, API usage rules and what is not permitted
•Taxation of trading income in India: business income versus capital gains, speculative treatment, audit thresholds
•Record-keeping and reporting obligations
•Indian payments infrastructure: UPI, NPCI rails, IMPS, NEFT, RTGS and how money actually moves
•Digital lending: underwriting models, credit bureau data, RBI digital lending guidelines
•Account Aggregator framework and consent-based financial data sharing
•Wealth-tech and broking business models, and where revenue actually comes from
•KYC, AML and compliance obligations for a FinTech product
•Building a FinTech product: regulatory perimeter, partnerships, licensing routes
10 Module 10 — Portfolio construction and strategy management View
•Combining multiple strategies: correlation analysis, capital allocation, rebalancing
•Capacity estimation: how much capital a strategy can absorb before edge decays
•Strategy lifecycle: research, validation, deployment, monitoring, decay detection, retirement
•Performance attribution: separating skill from market beta and luck
•Benchmark selection and honest comparison against a passive alternative
•Investor reporting standards if you manage external capital
•The psychology of systematic trading: adherence, intervention, and why discretion usually destroys returns
11 Module 11 — Capstone: build, validate and defend a strategy View
•Week 1–4: market and instrument selection, economic hypothesis stated and defended
•Week 5–9: data pipeline built, cleaned and validated; signal implemented
•Week 10–14: rigorous backtest with realistic costs, walk-forward validation and robustness testing
•Week 15–18: risk framework, position sizing and drawdown rules written and tested
•Week 19–21: broker API integration, paper trading and live monitoring with kill switches
•Week 22–24: strategy book, risk memo and results defended before mentors, including a written account of what did not work
•Deliverables: backtested strategy with honest metrics, risk and sizing framework, automated execution system, and a FinTech domain portfolio piece

Tools you will work in

PythonPandas / NumPyJupyterBacktesting frameworksBroker APIsSQLExcel

What you walk out with

OUTCOME
A backtested strategy with honest metrics
OUTCOME
A written risk and sizing framework
OUTCOME
A FinTech domain portfolio piece

Roles this prepares you for

Salary depends on your city, experience and the strength of your project, so we do not publish package figures we cannot substantiate.

Quantitative analystAlgo trading associateFinTech product analystRisk analyst

Fees and payment

TOTAL PROGRAM FEE
₹49,999
All inclusive. No registration or material charges.
PAYMENT PLAN
₹10,000 × 6
No interest, no processing fee. First instalment at enrolment.
✓  Live classes with a working practitioner
✓  Lifetime access to every recording
✓  Weekly doubt-clearing session
✓  Certified project reviewed by a mentor
✓  Rejoin any future batch free if you fall behind

FinTech & Algorithmic Trading training across India

Batches run live online, so the class is identical wherever you join from — same trainer, same time, same fee. No city-based pricing. Classroom batches run in Kolkata, Asansol and Durgapur.

TIER 1 CITIES

Ahmedabad, Bengaluru, Chennai, Delhi, Hyderabad, Kolkata, Mumbai, Pune.

TIER 2 CITIES

Agra, Ajmer, Amritsar, Bhopal, Bhubaneswar, Chandigarh, Coimbatore, Dehradun, Faridabad, Ghaziabad, Gwalior, Indore, Jaipur, Jamshedpur, Kanpur, Kochi, Lucknow, Mysore, Nagpur, Nashik, Patna, Raipur, Surat, Vadodara, Visakhapatnam.

TIER 3 CITIES

Bathinda, Bikaner, Cuttack, Etawah, Gandhinagar, Hajipur, Hosur, Jhansi, Junagadh, Madurai, Mathura, Meerut, Rajahmundry, Rohtak, Roorkee, Salem, Udaipur, Vijayawada.

See all locations across India →

FinTech & Algorithmic Trading — frequently asked questions

Still unsure? Call an advisor on +91 90888 39993.

What is the FinTech & Algorithmic Trading course fee in India?+

The FinTech & Algorithmic Trading course fee at Careers Ninza is ₹49,999, inclusive of everything. You can pay it as ₹10,000 × 6 under our No-Cost EMI plan, with no interest and no processing charge. The fee covers all live classes, lifetime recordings, weekly doubt-clearing sessions, assessments, the mentor-reviewed capstone project and placement assistance.

Is FinTech & Algorithmic Trading available online across India?+

Yes. Every batch runs live online, so you can join FinTech & Algorithmic Trading from anywhere in India. We currently have students from Kolkata, Asansol, Durgapur, Siliguri, Howrah, Gwalior, Patna, Ranchi, Bhubaneswar, Guwahati, Pune, Mumbai and many other cities, plus learners outside India. On-site delivery is available in Kolkata, Asansol and Durgapur, and on request in other cities for corporate or campus groups.

How long is the FinTech & Algorithmic Trading course and what is the weekly time commitment?+

FinTech & Algorithmic Trading runs for 6 months. Plan on six to eight hours a week: two live weekday evening sessions plus project work, with occasional weekend workshops. Working professionals complete it without taking leave.

Do I need prior experience to join FinTech & Algorithmic Trading?+

Beginner to job-ready. The course starts from first principles and the mentor calibrates pace to the batch. No technical or coding background is required. If a specific prerequisite genuinely matters for your goal, the advisor will tell you honestly on the counselling call rather than take the enrolment.

Who teaches FinTech & Algorithmic Trading?+

A working practitioner in the field, not a full-time trainer. Careers Ninza has more than 100 industry mentors who teach while still doing the job, so the examples come from current work rather than a textbook. You can ask which mentor is assigned to your batch before enrolling, or meet them at a free live masterclass.

What will I build during the FinTech & Algorithmic Trading course?+

One real capstone project, scoped in the first fortnight and carried through every module: a backtested strategy with honest metrics. It is reviewed by your mentor with written feedback, and it is what interviewers or clients actually discuss with you afterwards.

Is the FinTech & Algorithmic Trading certificate recognised?+

You receive a verifiable Careers Ninza industry certificate with a unique ID and a public verification link, issued by Ninza Career Solutions Pvt. Ltd. once your project is accepted. It is an industry certificate, not a government-accredited degree or diploma, and we say so plainly. Its weight comes from the reviewed project behind it.

Does FinTech & Algorithmic Trading include placement support?+

Yes. Long-term programs include portfolio review, resume and LinkedIn clinics, mock interviews with working professionals, and referrals to our hiring partner network, for twelve months after completion. There is no success fee and no income-share agreement. We do not guarantee employment, and we would be cautious of any institute that does.

What career roles does FinTech & Algorithmic Trading prepare me for?+

Quantitative analyst, Algo trading associate, FinTech product analyst, Risk analyst. Salary depends on your city, prior experience and the strength of your project, so we do not publish package figures we cannot substantiate; an advisor will give you an honest band for your target role and city.

What if I miss classes or fall behind in FinTech & Algorithmic Trading?+

Every session is recorded and stays yours for life. There is a weekly doubt-clearing slot where you can bring your own work, and if you fall too far behind you may rejoin any future batch of FinTech & Algorithmic Trading free of charge, with no conditions.

How is FinTech & Algorithmic Trading at Careers Ninza different from a recorded online course?+

It is live and never pre-recorded. There is no cheaper self-paced tier, because removing the live class removes the reason the course works. Batches are capped so a quiet student still gets asked questions, and the mentor reviews your project personally rather than auto-marking a quiz.

Can I see a FinTech & Algorithmic Trading class before paying?+

Yes. Careers Ninza runs free live masterclasses every week. Reserve a seat, watch a practitioner teach for ninety minutes, ask questions, and then decide. To enrol or to ask which batch suits you, call +91 90888 39993 or message us on WhatsApp.

How do I enrol in FinTech & Algorithmic Trading?+

Call +91 90888 39993, email [email protected], or send an enquiry on WhatsApp from this page. An advisor confirms whether FinTech & Algorithmic Trading fits your goal, then sends written confirmation of your batch, the fee and No-Cost EMI of ₹10,000 × 6. Seats are capped, so batches close once full.

FinTech & Algorithmic Trading across India — city questions

Same class, same fee, same mentor, wherever you are.

Is FinTech & Algorithmic Trading available in my city?+

Yes. Every batch runs live online, so learners across India attend the same class in real time — including Visakhapatnam, Guwahati, Patna, Ahmedabad, Surat, Vadodara, Rajkot, Faridabad, Gurugram, Bengaluru and every other city and town. There is no separate recorded version for students outside Kolkata, and no city has a different curriculum.

Does the FinTech & Algorithmic Trading course fee change by city?+

No. The fee is ₹49,999 everywhere in India, with the same No-Cost EMI of ₹10,000 × 6. We do not price differently for metro and non-metro learners, and there are no travel or centre charges for online batches.

Do you have a classroom centre near me?+

Classroom batches run in Kolkata, Asansol and Durgapur. Everywhere else in India is served by live online delivery, which most working professionals and students prefer. On-site delivery in other cities is available for corporate teams and college groups on request.

What are the batch timings for people in different time zones within India?+

India runs on a single time zone, so timings are identical nationwide: weekday evening sessions from 7pm IST with weekend workshops. Every session is recorded and stays yours for life, so a late shift or travel never costs you a module.

Will placement support help me find work in my own city?+

Yes. Our hiring partner network covers roles across Indian cities, and a growing share of openings are remote and open to candidates anywhere in India. We share your profile only with your consent, for a specific role. We do not guarantee employment.

Other programs you might consider

ENTREPRENEURSHIP Ecompreneurship 6 months · ₹49,999 MARKETING Digital Marketing Entrepreneur 6 months · ₹49,999 GROWTH Performance Marketing & Growth Hacking 3 months · ₹24,999

Seats in every batch are capped

Talk to an advisor for fifteen minutes. We will tell you whether FinTech & Algorithmic Trading fits your goal — or which program does instead.

APPLY NOW CALL +91 90888 39993