VBA For Financial Engineering & Modeling - Online 20 Hours Course

VBA For Financial Engineering & Modeling - Online 20 Hours Course

LINK: http://www.wiziq.com/course/19620-vba-for-financial-engineering-and-modeling

Join our VBA for financial engineering course (http://www.wiziq.com/course/19620-vba-for-financial-engineering-and-modeling) & get 15% discount. Ask for discount code, email - info@qcfinance.in.

Website - http://qcfinance.in/
Youtube Channel - http://www.youtube.com/user/shivbhaktajoshi

With the above registration, you will also get access to all updates and premium membership at Qcfinance.in.

Class	Topic	Duration
1	Introduction to Programming in VBA	2 Hours
2	Introduction to Quant Corporate Finance	2 Hours
3	Data Types, Ranges & Cell Arrays	2 Hours
4	Logical Operators & Control Flow	2 Hours
5	Distributions, FRM, VAR	2 Hours
6	Techniques For Handling Missing Data	2 Hours
7	Investment Banking Quant	2 Hours
8	Data Pulling Into Excel	2 Hours
9	Portfolio Optimization	2 Hours
10	Econometrics: Multiple Regression and Logistic Regression	2 Hours

Course highlights:

• Learn VBA without learning programming.
• Feel the same as you are on an IB desk.
• A demo class can be registered as per convenience.
• Doubt clearing classes available.
• Option for one on one classes available on requests.

Course plan:

1. Quant Corporate Finance (Investment Banking).
2. Quant Equity (Equity strategies and indices).
3. Time Series.
4. Yield curve/Fixed Income/ABS.
5. Binomial pricing MC/Hull white/BS/exotic options KMV.
6. Portfolio at risk.

From VBA Programming point of view (Divided into following areas):

1. Dependencies and removing arrows.
2. Picking non blank cells.
3. Selecting sheet and changing color.
4. Selection vs. entire sheet.
5. For loops, if end if loops, exiting loop, placing end, nestled for loop.
6. Importing values from another sheet, without opening.
7. Change manual to automatic formula computation.
8. Data tables with one and two variables using VBA.
9. With Command.
10. Set Command.
11. := used where?
12. Selecting cell with specific values.
13. Data tables.
14. Combining array in a single cell using delimiter.
15. type data validation, playing with ranges.
16. VBA editing of data validation.
17. playing with string to get the last value.
18. playing with axis of charts formatting.
19. Functions with many inputs.
20. On Error.
21. Option Explicit others.
22. Selective clearing arrays rows ranges by clear command.

Some key points about the course:

• Requires absolutely no knowledge of programming.
• Provide introduction about all Quantitative roles in Investment Banking.
• Highly flexible and tailored as per needs of individual (10-50 % Quant Finance & 10-50% VBA).
• Sensitization on derivative, Quant Equity corporate IB, fixed income, Monte Carlo.
• Feel the same as you while you are on the IB desk.
• Examples with real data to enhance your Financial IQ.
• Under the applicability and use on Bloomberg or Reuters websites (Introduction to tickers, RIC).
• Real recent examples and real cases which are hot in the market.
• New Interpretation, terminologies, and basic IQ for the subject covered.
• Helpful for passing FRM, CFA, BAT exams also prepares for Master level studies in Finance or career change.
• Right mix of data handling, scripting, mathematical skills.
• Contains right blend of learning and practice (Ratio 6:4).

Below are the video description of the course and the ppts used:

Addon Module on Quant Corporate Equity. This could include quant index, beta computations, different style of index, equity derivatives, importance of volume traded, value growth differences, emerging and developed markets relations, how index are made, using ric ticker, etc equity database research, etc.

Addon Module on Financial Risk: This could include Monte-Carlo, VAR, BS, Copulas pricing cdo, pricing exotic options, Modified BS models, EVT distributions, VLOOKUP, long data tables etc.

List of commands that we will use with references for self-study:
Param Array: Challenges and use of param array for dynamic number of inputs. Param array is itself added with GOTO command.

GOTO: Referring string named ranges Application. 

GOTO Reference:=abc2
'Range("qrs").Value = Selection Value
Application GOTO.
http://msdn.microsoft.com/en-us/library/office/ff839232.aspx.

The above command is used to go to a named range and select it. This is slower way to do the same.


Data Tables: Making data tables in vba, how to clear all update values, in other words enable/disable tables.

Setting default values from range to other named ranges: 2 column range and going to name using goto command and also storing value as string... clearing old values and difference between if error go to next and if error go to ext.

With Command: Use to act several attributes to an object in one go: http://msdn.microsoft.com/en-us/library/wc500chb(v=vs.80).aspx

The On Error Statement: http://www.cpearson.com/excel/errorhandling.htm.

Application.Union: Takes union of ranges.

This can be used to take union of ranges that are defined at different places.
http://www.cpearson.com/excel/BetterUnion.aspx

Call function in VBA is used to call functions based on name and parameters
http://msdn.microsoft.com/en-us/library/sxz296wz(v=vs.80).aspx

http://msdn.microsoft.com/en-us/library/office/aa204537(v=office.11).aspx
Expression will calculate the range means I think it is like replacing = with =

Sub procedure
http://msdn.microsoft.com/en-us/library/dz1z94ha(v=vs.80).aspx

.activate
http://msdn.microsoft.com/en-us/library/office/ff194565.aspx.

Function overloading in MATLAB is an interesting area, it is like defining function adhoc that will be used that time only and I think activated during the lines are used

Referencing in VBA (byRef ByVal):
http://msdn.microsoft.com/en-us/library/bb190882(office.11).aspx

Excel functions used commonly:
By val / reference
http://www.techonthenet.com/excel/formulas/index_vba.php
http://roymacleanvba.wordpress.com/2009/05/01/byref-and-byval/

.add
http://msdn.microsoft.com/en-us/library/office/aa221688(v=office.11).aspx


Other courses that you can refer to:Demo Course structure:
http://www.vtc.com/products/Microsoft-Visual-Basic-for-Applications-(VBA)-Tutorials.htm


Given below is the playlist that will have all the videos related the course:

Contact Details: shivgan@qcfinance.in, arpit@qcfinance.in (Arpit).

Contact Us for more details: info@qcfinance.in.

Equity / Corporate Quant Finance

I plan to start course on Quant Equity. This could include quant index, beta computations, different style of index, equity derivatives, importance of volume traded, value growth differences, emerging and developed markets relations, how index are made, using ric ticker, etc equity database research, etc.


Corporate quant fiance will be another important area.


1) Inter-corporate investment forms the heard of IB.

2) Minority interest is the main thing here. Proforma MI and how it is placed in IS BS.

3 Three types of securities and effect of the financials in recession is helpful.

4) Since the operations of the company are most of time abroad aspects of accounting needs to learned. Currency might play a very important role.

5) Adjustments for long lived assets for creating a future BS IS is another important aspect one must know.

This balances the approach on CFA L2 and BAT.

These form an integral part of the IB learning. A 20 page slide would be good enough on this.


Target videos to be developed:

1. Advanced Equity 15min
2. Methods of Analysis 15min
3. Corporate Finance 15min
4. Other Finance 15min
5. M&A 15min
6. Pensions 15min
7. Translations 15min
8. Ethics 15min

MATLAB External Links on Indexing, Structure & Functions

Join our MATLAB for financial engineering course (http://www.wiziq.com/course/7225-matlab-for-financial-engineering) & get 15% discount. Ask for discount code, email - info@qcfinance.in

Three major topics:

Indexing MATLAB

http://www.mathworks.in/company/newsletters/articles/Matrix-Indexing-in-MATLAB/matrix.html

http://www.mathworks.in/help/matlab/math/matrix-indexing.html;jsessionid=fb0677fdc592efa46a4cecf14a0e?s_tid=doc_12b

http://www.psi.toronto.edu/~vincent/matlabindexrepmat.html

http://stackoverflow.com/questions/4842512/matlab-indexing-question

http://www.mathworks.in/help/matlab/learn_matlab/array-indexing.html

http://stackoverflow.com/questions/11023010/matlab-indexing-into-2d-array-using

http://www.mathworks.in/help/matlab/cell-arrays.html

http://www.mathworks.in/help/matlab/indexing.html

http://www.mathworks.com/matlabcentral/newsreader/view_thread/270741

http://blogs.mathworks.com/steve/2008/01/28/logical-indexing/

http://blogs.mathworks.com/loren/2006/06/21/cell-arrays-and-their-contents/#3

http://www.eng.utah.edu/~cs5310/cs1000/notes11.html

http://www.psi.toronto.edu/~vincent/matlabindexrepmat.html

http://www.mathworks.in/help/matlab/math/matrix-indexing.html?s_tid=doc_12b


Structure

http://stackoverflow.com/questions/1882035/how-do-i-access-matlab-structure-fields-within-a-loop

http://www.mathworks.in/help/matlab/structures.html

http://www.mathworks.in/help/matlab/ref/struct.html

http://www.mathworks.in/help/matlab/ref/getfield.html

http://www.cs.utah.edu/~germain/PPS/Topics/Matlab/structures.html

http://www.atmos.washington.edu/~mitchell/matlab_strings.html

http://www.mathworks.in/help/stats/statset.html

http://www.mathworks.in/help/matlab/ref/cell2struct.html

http://www.mathworks.com/matlabcentral/fileexchange/22752-compare-structures

http://www.mathworks.in/help/matlab/matlab_prog/cell-vs-struct-arrays.html


Array & Functions

http://web.cecs.pdx.edu/~gerry/MATLAB/programming/basics.html

http://amath.colorado.edu/computing/Matlab/Tutorial/Programming2.html

http://www.math.tamu.edu/~fnarc/psfiles/matlab_fn.pdf

http://www.mathworks.com/help/matlab/matlab_prog/create-functions-in-files.html

http://ws2.binghamton.edu/fowler/fowler%20personal%20page/EE521_files/MATLAB%20Functions.pdf

http://ocw.mit.edu/resources/res-18-002-introduction-to-matlab-spring-2008/other-matlab-resources-at-mit/tutorial06.pdf

http://www.ele.uri.edu/Courses/ele541/tutorials/matlab.html

http://www.life.illinois.edu/mcb/419/matlab/html/tut3.html 

http://en.wikibooks.org/wiki/MATLAB_Programming/Struct_Arrays

http://www.mathworks.in/products/matlab/examples.html?file=/products/demos/shipping/matlab/strucdem.html


Application:

Portfolio

http://www.mathworks.in/discovery/portfolio-optimization.html

http://www.mathworks.in/help/finance/examples/mean-variance-efficient-frontier.html

http://www.mathworks.in/help/finance/portcons.html


Regression

http://www.mathworks.in/help/stats/regress.html

http://www.mathworks.in/help/stats/understanding-linear-regression-outputs.html

http://www.mathworks.in/help/matlab/data_analysis/linear-regression.html

http://www.mathworks.in/help/econ/examples/time-series-regression-vii-forecasting.html

http://dynsys.uml.edu/tutorials/Regression_Analysis/regression_analysis_tutorial_012005.pdf

http://chemwiki.ucdavis.edu/@api/deki/pages/2962/pdf

http://www.swarthmore.edu/NatSci/echeeve1/Ref/Matlab/CurveFit/LinearCurveFit.html

http://www.mathworks.in/help/nnet/ref/regression.html

SAS Statistical Business Analyst Using SAS 9 Prep

SAS Statistical Business Analyst Using SAS 9 Prep

Website - http://qcfinance.in/

YouTube Channel - http://www.youtube.com/user/shivbhaktajoshi

Check out The Link: http://qcfinance.in/sas-business-analytic-course/

Exam topics include:

ANOVA

• Verify the assumptions of ANOVA.
• Analyze differences between population means using the GLM and TTEST procedures.
• Perform ANOVA post hoc test to evaluate treatment effect.
• Detect and analyze interactions between factors.

Linear Regression

• Fit a multiple linear regression model using the REG and GLM procedures.
• Analyze the output of the REG procedure for multiple linear regression models.
• Use the REG procedure to perform model selection.
• Assess the validity of a given regression model through the use of diagnostic and residual analysis.

Logistic Regression

• Perform logistic regression with the LOGISTIC procedure.
• Optimize model performance through input selection.
• Interpret the output of the LOGISTIC procedure.
• Score new data sets using the LOGISTIC and SCORE procedures.

Prepare Inputs for Predictive Model Performance

• Identify potential problems with input data.
• Use the DATA step to manipulate data with loops, arrays, conditional statements and functions.
• Reduce the number of categorical levels in a predictive model.
• Screen variables for irrelevance using the CORR procedure.
• Screen variables for non-linearity using empirical logit plots.

Measure Model Performance

• Apply the principles of honest assessment to model performance measurement.
• Assess classifier performance using the confusion matrix.
• Model selection and validation using training and validation data.
• Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection.
• Establish effective decision cut-off values for scoring.

Prerequisite Basic Concepts 

• descriptive statistics
• inferential statistics
• steps for conducting a hypothesis test
• basics of using your SAS software

Introduction to Statistics 

• examining data distributions
• obtaining and interpreting sample statistics using the UNIVARIATE and MEANS procedures
• examining data distributions graphically in the UNIVARIATE and SGPLOT procedures
• constructing confidence intervals
• performing simple tests of hypothesis

t Tests and Analysis of Variance 

• performing tests of differences between two group means using PROC TEST.
• performing one-way ANOVA with the GLM procedure.
• performing post-hoc multiple comparisons tests in PROC GLM.
• performing two-way ANOVA with and without interactions.

Linear Regression 

• producing correlations with the CORR procedure.
• fitting a simple linear regression model with the REG procedure.
• understanding the concepts of multiple regression.
• using automated model selection techniques in PROC REG to choose from among several candidate models.
• interpreting models.

Linear Regression Diagnostics 

• examining residuals
• investigating influential observations
• assessing collinearity

Categorical Data Analysis 

• producing frequency tables with the FREQ procedure
• examining tests for general and linear association using the FREQ procedure
• understanding exact tests
• understanding the concepts of logistic regression
• fitting univariate and multivariate logistic regression models using the LOGISTIC procedure

Predictive Modeling 

• business applications
• analytical challenges

Fitting the Model 

• parameter estimation
• adjustments for oversampling

Preparing the Input Variables 

• missing values
• categorical inputs
• variable clustering
• variable screening
• subset selection

Classifier Performance 

• ROC curves and Lift charts
• optimal cutoffs
• K-S statistic
• c statistic
• profit
• evaluating a series of models

Link: http://support.sas.com/certify/creds/sba.html#t2

SAS Base Certification Preparation Advice Strategy

Data Step

Proc Step

Library referring

VB is more to excel linked work, which handles less data, like 2-3 lack entries
SQL is generic and common to all MATLAB SAS VBA
it is database methodology
R is a free solution for MATLAB SAS
SAS is more database manatment less maths
MATLAB is more maths less database

Market Risk Credit Risk FRM L2 (Book1) Prep Strategy Info [Learn FRM using MATLAB/R/VBA]

Market Risk FRM L2

Three most interesting areas in Market Risk Uni variate and Multi Variate :

1. EVT
2. Multi Variate Models
3. Back Testing
4. Monte Carlo
5. Copula

4 Chapters in which the area is divided:

1. Uni variate
2. Multivariate
3. Volatility
4. MBS: OAS, etc. Pre payment, trenching, etc

The first two might be theoretical and more maths, but the last 2 are seen extensively in the market. Volatility index is something which is often seen and an important area.

Credit risk

A highly practical subject

1. Includes CDO, CDS, pre payments, etc
2. default probability and CDS
3. sovereign rating: GDP, currency and debt of the nation
4. measuring default from market prices
5. spread and default
6. merton model
7. currency swap interest rate swap
8. CDO structure trenching: junior senior

Other topics

Hedge fund risk / portfolio risk

MATLAB VAR:

http://gloria-mundi.com/library_journal_view.asp?journal_id=8769

http://www.mathworks.com/matlabcentral/fileexchange/32154-estimation-value-at-risk-by-using-conditional-copula-garch

http://www.mathworks.com/help/finance/conditional-value-at-risk-portfolio-optimization.html

http://www.mathworks.com/help/finance/portvrisk.html

http://www.docstoc.com/docs/73976635/Matlab-Code-for-Value-at-Risk-Methods

http://www.analyticsresearch.net/Documents/VALUE%20AT%20RISK.new4practical.pdf

http://datavir.com/?p=304

http://www.thetaris.com/wiki/Value_at_Risk

http://www.mathfinance.cn/value-at-risk-estimation-with-copula/

http://www.sfu.ca/~rgencay/evim.pdf

https://editorialexpress.com/cgi-bin/conference/download.cgi?db_name=ESAM2011&paper_id=145

http://phd.london.edu/pnath.phd98/MatlabUtilities.htm

R for Financial Engineering Course on Applying R in Quantitative Finance/ Analytics & Projects at qcfinance.in

R for Financial Engineering / Quantitative Finance

Link: http://www.wiziq.com/course/77609-introduction-to-r

Website - http://qcfinance.in/r-for-financial-engineering-course/

YouTube Channel -http://www.youtube.com/user/shivbhaktajoshi

Playlist: http://www.youtube.com/playlist?list=PL_-KSXJS5pxMr4kv8IiYE28FNiHCoiplP

R is mix of MATLAB and SAS, if I relate to the other 2 tools used in Finance.

R is having mix of maths and data handling powers of SAS and given that it is open source and the best choice for research.

R is MATLAB when it comes to syntax.

When it comes to data handling it uses RMySQL in the very same way as SAS does.

Hence, it does things that SAS+MATLAB do for free.

SQL has inherent limitations of doing quant computations, and MATLAB is heave and requires too much resources. MATLAB is not very good to deal with SQL at system level.

But to start with, one also needs to know VBA for entry level and for easy task, VBA is preferred.

Divisions:

1. 10 hours on handling data, functions, indexing, structures, data linkages, importing, SQL, etc
2. 10 hours on Financial Commands, tools, pre-built libraries.

I will discuss in detail about R and its application in this post.

Keep checking it for updates.

Ref: R in a Nutshell

For getting some info you can join my MATLAB Course at:

http://stockcreditfinancecfa.blogspot.com/2012/07/50-hours-course-in-matlab-for-finance.html

Join our MATLAB for financial engineering course (http://www.wiziq.com/course/7225-matlab-for-financial-engineering) & get 15% discount. Ask for discount code, e-mail:- info@qcfinance.in.

Course based on:

http://www.unicom.co.uk/quant-finance/R-for-Finance.php

http://www.optirisk-systems.com/papers/FinancewithR.pdf

http://www.optirisk-systems.com/events/finance-with-r.asp

https://www.rmetrics.org/ebooks-basicr

http://www.r-bloggers.com/search/finance

http://www.rinfinance.com/RinFinance2009/presentations/ChicagoWuertz.pdf

http://blog.revolutionanalytics.com/2011/11/course-financial-data-modeling-and-analysis-in-r.html

http://braverock.com/brian/R/PerformanceAnalytics/html/PerformanceAnalytics-package.html

http://www.quantmod.com/documentation/00Index.html

http://tradeblotter.files.wordpress.com/2012/06/uw_r_seminar_may_2012.pdf

Introduction to R

• Some background on R, how to obtain and how to install it.
• R documentation and help and web resources available.
• R Console and R GUI.
• Data import, data types and variables.
• Elementary import functions.
• Data types.
• Variable generation, inspection and modification.
• Graphics in R.
• Command "plot" and where to plot.
• Setting the par.
• High-level and low-level plotting functions legends.
• Math in plots.  
• Hands-on Session—data handling & graphics.
• Simulations in R.
• Simulating from given distributions.
• Risk management basics.
• Data Structures in R.
• Review of types of objects.    
• Additional object types.
• Basics of the R Language. 
• Conditionals & Loops. 
• Writing R Functions: Basic functions.
• Object-orientation.
• Debugging.
• Application in Finance.
• Calculating Value at Risk.
• Packages for Portfolio Optimization. 

Functions of R/R Commands to be covered in the course:
Caution: Always save workspace.

Use “c” when you want to use it a signal in black scholes formula.

read.table function will be used to get the data into R (/\ used opposite

than the one used in windows)

http://www.ats.ucla.edu/stat/r/faq/inputdata_R.htm.

Example 1

> test.txt <- read.table ("C:/Documents and Settings/shivbhakta.joshi/My

Documents/test.txt", header=T)

> print(test.txt)

> column <- test.txt[,c('make','price')].

Example 2

grb <- read.table ("C:/Documents and Settings/shivbhakta.joshi/My

Documents/GRB_afterglow.dat", header=T, skip=1)

x <- (grb[,1]).

Grabbing data from tables into a single dimensional array
x <- (grb[,1])

y <- log(grb[,1]).

Another way to create returns for stock daily return is

f=dft$age/dft$price (using dollar sign extraction)

Deleting column http://www.sr.bham.ac.uk/~ajrs/R/r-manipulate_data.html

> add <- add[-2] to delete the element 2nd

Adding data to array busing column command

> add <- c(1,d).

Using Data frames
Data.frame is used to combine the arrays back into table format

people = data.frame (x,y)

Write.CSV command executed

write.csv(people,"filename.csv")

grb <- read.table ("C:/Documents and Settings/satyadhar.joshi/My

Documents/GRB_afterglow.dat", header=T, skip=1)

x <- (grb[,1])

y <- log(grb[,1])

people = data.frame (x,y)

write.csv(people,"filename.csv")

These methods can be used to compute beta from columns of a csv file.

Working formula of Black Scholes

BS <-

function(S, K, T, r, sig, type="C"){

d1 <- (log(S/K) + (r + sig^2/2)*T) / (sig*sqrt(T))

d2 <- d1 - sig*sqrt(T)

if(type=="C"){

value <- S*pnorm(d1) - K*exp(-r*T)*pnorm(d2)

}

if(type=="P"){

value <- K*exp(-r*T)*pnorm(-d2) - S*pnorm(-d1)

}

return(value)

}

Black Scholes <- function(s, k, r=.1, t=5, sigma=.9,call=TRUE) {

#calculate call/put option

d1 <- (log(s/k)+(r+sigma^2/2)*t)/(sigma*sqrt(t))

d2 <- d1 - sigma * sqrt(t)

ifelse(call==TRUE,s*pnorm(d1) - k*exp(-r*t)*pnorm(d2),k*exp(-r*t)

* pnorm(-d2) - s*pnorm(-d1))

}

Predefined Black schools model:

http://shafik.net/~shafik/FinancialEngineering/Code/

black_scholes ("c",5600,5600,.1,.08,.22 )

this depicts use of functions in R.

Merton Distance to Default (different as per needs)

(modified from Black Scholes formula, still

unsure about pnorm() function)

d1 <- (log(s/k)+(sigma^2/2))/(sigma)

pnorm(d1)

3 files are needed to run the demos which are available on internet by various authors:

1. black_scholes.R
2. GRB_afterglow.dat
3. test.txt.

ARIMA and Forecasting Using R

Commands:

library(Quandl)
> Quandl.auth("1LkmpypqJskJzKcpd2TV")

>stockData=Quandl("YAHOO/INDEX_GSPC", start_date="2004-01-01", end_date="2014-04-17")

stockData[,7]

// ran Arima here:
results2=arima(stockData[,7], order = c(3,0,0))

fit <- arima(myts, order=c(p, d, q)

fit <- arima(stockData[,7],order = c(3,0,0))

results=arima(stockData[,7])

fit <- arima(stockData[,7],order = c(3,0,0))

predict(arima(stockData[,7], order = c(3,0,0))

results=arima(stockData[,7])

x2=(forecast(fit, 100))

plot(x2).


Excel and R:
http://rcom.univie.ac.at/download.html#statconnDCOM
http://www.r-bloggers.com/a-million-ways-to-connect-r-and-excel/
http://cran.r-project.org/web/packages/XLConnect/vignettes/XLConnect.pdf
http://cran.r-project.org/web/packages/XLConnect/XLConnect.pdf
https://stat.ethz.ch/R-manual/R-patched/library/base/html/getwd.html


Playing with arrays and matrix
http://cran.r-project.org/doc/FAQ/R-FAQ.html#Why-do-my-matrices-lose-dimensions_003f
https://stat.ethz.ch/pipermail/r-help/2008-February/154088.html
http://stat.ethz.ch/R-manual/R-devel/library/base/html/rev.html.

ARIMA and Predict statement
http://stat.ethz.ch/R-manual/R-patched/library/stats/html/predict.arima.html
http://people.duke.edu/~rnau/411arim.htm
http://www.statmethods.net/advstats/timeseries.html
http://stackoverflow.com/questions/14272937/time-series-prediction-using-r
http://www.inside-r.org/packages/cran/forecast/docs/forecast.Arima.

Market check
http://www.bloomberg.com/quote/SPX:IND/chart.

Other good References

http://heather.cs.ucdavis.edu/~matloff/132/NSPpart.pdf.

One on One Customized Training:

qcfinance.in believes in personalized touch so that our clients are completely satisfied with our service. In this regard, we offer One on One Customized Training to our clients.

These Trainings are provided on request by our clients & are customized according to their individual needs.

The course structure & timings for these training are highly flexible, classes are scheduled as per the convenience of our clients. 

Contact Us for More details: info@qcfinance.in.