Python for Machine Learning in Finance
₹3325/-₹13299/-
75% OFF
Get for ₹2993 with Course Bundle
- Live Trading
- Learning Track
- Prerequisites
- Syllabus
- About author
- Testimonials
- Faqs
Live Trading
- Backtest, analyse the strategy returns and drawdowns, paper trade and live trade machine learning strategy
- Describe machine learning and its applications in finance
- List and implement common tasks in machine learning such as feature creation, training, forecasting, and evaluation in a step-by-step fashion
- Explain and implement accuracy, f1-score, recall and confusion matrix and R-squared
- Implement the train-test split for time series data

Skills Covered
Machine Learning
- Train-test split
- Training an ML model
- Forecasting
- Evaluation
Stats & Maths
- R-Squared
- Accuracy
- Recall
- F1-Score
Python
- Numpy
- Pandas
- Matplotlib
- Sklearn

learning track 4
This course is a part of the Learning Track: Machine Learning & Deep Learning in Trading Beginners
Course Fees
Full Learning Track
These courses are specially curated to help you with end-to-end learning of the subject.
Course Features
- Community
Faculty Support on Community
- Interactive Coding Exercises
Interactive Coding Practice
- Trade & Learn Together
Trade and Learn Together
- Get Certified
Get Certified
Prerequisites
No experience is required except for a very basic understanding of financial markets such as how to place orders with your broker. You should be curious to explore the application of machine learning in finance. You can do this course even if you have never coded before. However, to replicate the sample trading strategy shared in the course, you might need to display some code reading and interpretation skills.
Syllabus
- IntroductionMachine learning has myriad applications in various industries, including finance. In this section, you will acquaint yourself with the course structure, and the various teaching tools used in the course: videos, quizzes, and strategy codes. The interactive methods used help you to not only understand the concepts, but also how to implement the strategies.
Machine Learning Overview
In this section, you will understand how machine learning is used to solve problems which cannot be solved by traditional computer algorithms. You will also see its applications in different finance domains.Introduction to Python
This section will help you update your knowledge of Python with simple exercises on implementing functions, and manipulating dataframes using Numpy and Pandas libraries. The Quantra environment ensures that you don’t have to install anything for the Jupyter notebooks to function.Uninterrupted Learning Journey with Quantra2mNeed for Python3m 7sPreference for Python2mFunctionality of Python2mHow to Use Jupyter Notebook?2m 5sPrint Statement5mMy First Jupyter Notebook10mGetting Started with Interactive Exercises5mOperations and Functions in Python10mDivide Two Numbers5mPandas Dataframe2m 22sFunction Call5mDataFrame Axis Label2mDataFrame and Basic Functionality10mDataFrame Syntax2mDropping/Deleting Columns2mCreate Pandas DataFrame5mDataFrame Indexing2mPrint Columns2mAccess Elements of a DataFrame5mAdd New Column to a DataFrame5mSet Column as Index5mAdd Values of a Column5mAdditional Reading10m- Financial Market Data and VisualisationAn important component of a successful strategy is the data set used. In this section, you will learn how to import the correct data from various web resources, so that you can work on your own unique strategy.Importing Data1m 44sCorrect Syntax for Importing Stock Data2mImporting Time Series Data10mImport Data from Yahoo! Finance5mData Visualisation10mPlot Line Graph5mPlot Bar Graph5mAdditional Reading10mFrequently Asked Questions10m
- Machine Learning TasksBefore you start using the machine learning model, you have to train it first. In this section, you will go through the steps in creating a machine learning algorithm and how its performance can be calculated.Machine Learning Tasks3m 28sOrder of Machine Learning Tasks2mJudging Performance of ML Model2m
Target Variable and Features
A target variable is something that a machine learning algorithm tries to predict. And in order to do that, it requires input, which are called features. This section will explain the concept of target and features through examples. You will also learn how to create target and features for an ML algorithm.Target Variable1m 59sChoose the Target Variable2mPre Reading Material10mFeatures4m 29sUse of Features2mCharacteristics of Features2mFeatures of an ML Model2mTarget and Features10mCreate a Target Variable5mCalculate RSI5mCalculate EMA5mCheck for Stationarity5mCorrelated Features5mAdditional Reading10mTest on Machine Learning Applications and Tasks10m- Machine Learning AlgorithmsThere are various types of machine learning algorithms. In this section, you will get an overview of each type of algorithm. You will also gain a basic understanding of which model to use for a particular problem statement.Machine Learning Algorithms10mModel for Predicting Direction2mModel for Predicting Price2mModel for Computing News Sentiment Score2m
Train-Test Split
The train-test split is the technique of splitting the data into two parts. One part is used to train the ML model, and the other part is used to evaluate how well the model is making the predictions. You will learn the correct way of dividing time-series data for the train-test split.Train-Test Split10mPerform the Train-test Split5mTest on Machine Learning Algorithms and Train-Test Splits10mTraining & Forecasting
The model is trained on the training data and then it is used to make forecasts. In this section, you will learn how to use the training and testing data with a machine learning model. For illustration, we implement a Random Forest classification model in Python and use it to make predictions.Metrics to Evaluate Classifier
Backtesting is used to separate good strategies from bad ones. In this section, you will learn how to analyse the performance of your strategy on the historical data through backtesting. You will also learn to develop and backtest a trading strategy in Python. You will further calculate certain metrics like strategy returns, annualised returns and Sharpe ratio.Evaluating Classifier Model Effectiveness4m 27sAccuracy of ML Model2mInterpretation of Accuracy2mMeaning of Confusion Matrix2mInterpreting Confusion Matrix2mPredicting Wrong Values2mFalse Positives in Confusion Matrix2mBeyond Accuracy4m 2sDescription of Precision2mPredicting Correct Signals2mDescription of Recall2mCalculation of Precision2mCalculation of Recall2mCalculation of f1 Score2mInference of Performance Metrics2mMetrics to Evaluate a Classifier10mConfusion Matrix5mClassification Report5mAdditional Reading10mTest on ML Algorithms and Metrics to Evaluate Classifier10mIntroduction to Backtesting
Backtesting is used to separate good strategies from bad ones. In this section, you will learn how to analyse the performance of your strategy on the historical data through backtesting.What is Backtesting?2m 22sBacktesting Technique2mDoes Past Reflect Future?2mHow to do Backtesting?2m 24sSteps in Backtesting2mEvaluate the Performance of Backtesting2mNeed for Backtesting2mDrawbacks of Backtesting2mStrategy Backtesting10mStrategy Returns5mAnnualised Returns5mSharpe Ratio5mAdditional Reading10m- Live Trading on BlueshiftThis section will walk you through the steps involved in taking your trading strategy live. You will learn about backtesting and live trading platform, Blueshift. You will learn about code structure, various functions used to create a strategy and finally, paper or live trade on Blueshift.Section Overview2m 19sLive Trading Overview2m 41sVectorised vs Event Driven2mProcess in Live Trading2mReal-Time Data Source2mBlueshift Code Structure2m 57sImportant API Methods10mSchedule Strategy Logic2mFetch Historical Data2mPlace Orders2mBacktest and Live Trade on Blueshift4m 5sAdditional Reading10mBlueshift Data FAQs10m
- Live Trading TemplateThis section includes a template of a trading strategy that can be used on Blueshift. The live trading strategy template is based on the strategy discussed in the course. You can tweak the code by changing securities or the strategy parameters. You can also analyse the strategy performance in more detail.Paper/Live Trading Random Forest Strategy10mFAQs for Live Trading on Blueshift10m
Metrics to Evaluate a Regressor
Along with the classifier model, you can also use the regression model to predict the target variables. In this section, you will be given a brief on linear regression and also how to analyse its performance by using the goodness of fit metric.Goodness of Fit4m 10sWhy Goodness of Fit2mError of a Good Model2mR-squared Value2mResidual Plot2mPattern in Residual Plot2mHigh R-squared Value2mR-Squared10mCalculate R-squared5mLimitations of R-squared2mAssumptions for Linear Regression10mHighest R-squared2mLinearity2mNot an Assumption for Linear Regression2mAutocorrelation2mResiduals2m- Run Codes Locally on Your MachineLearn to install the Python environment in your local machine.Python Installation Overview1m 59sFlow Diagram10mInstall Anaconda on Windows10mInstall Anaconda on Mac10mKnow your Current Environment2mTroubleshooting Anaconda Installation Problems10mCreating a Python Environment10mChanging Environments2mQuantra Environment2mTroubleshooting Tips For Setting Up Environment10mHow to Run Files in Downloadable Section?10mTroubleshooting For Running Files in Downloadable Section10m
- Course SummaryIn this section, you will go through the different concepts you learnt throughout the course. You will also be able to download all the strategy notebooks as a zip file. You can use these notebooks and modify their contents to create your own unique strategy.Course Summary3m 16sNext Steps10mPython Data and Codes2m
Registered Successfully!
You will receive webinar joining details on your registered email
Would you like to start learning immediately?
about author


Why quantra®?
- More in Less Time
Gain more in less time
- Expert Faculty
Get taught by practitioners
- Self-paced
Learn at your own pace
- Data & Strategy Models
Get data & strategy models to practice on your own
Faqs
- When will I have access to the course content, including videos and strategies?
You will gain access to the entire course content including videos and strategies, as soon as you complete the payment and successfully enroll in the course.
- Will I get a certificate at the completion of the course?
Yes, you will be awarded with a certification from QuantInsti after successfully completing the online learning units.
- Are there any webinars, live or classroom sessions available in the course?
No, there are no live or classroom sessions in the course. You can ask your queries on community and get responses from fellow learners and faculty members.
- Is there any support available after I purchase the course?
Yes, you can ask your queries related to the course on the community: https://quantra.quantinsti.com/community
- What are the system requirements to do this course?
Fast-speed internet connection and a browser application are required for this course. For best experience, use Chrome.
- What is the admission criteria?
There is no admission criterion. You are recommended to go through the prerequisites section and be aware of skill sets gained and required to learn most from the course.
- Is there a refund available?
We respect your time, and hence, we offer concise but effective short-term courses created under professional guidance. We try to offer the most value within the shortest time. There are a few courses on Quantra which are free of cost. Please check the price of the course before enrolling in it. Once a purchase is made, we offer complete course content. For paid courses, we follow a 'no refund' policy.
- Is the course downloadable?
Some of the course material is downloadable such as Python notebooks with strategy codes. We also guide you how to use these codes on your own system to practice further.
- Can the python strategies provided in the course be immediately used for trading?
We focus on teaching these quantitative and machine learning techniques and how learners can use them for developing their own strategies. You may or may not be able to directly use them in your own system. Please do note that we are not advising or offering any trading/investment services. The strategies are used for learning & understanding purposes and we don't take any responsibility for the performance or any profit or losses that using these techniques results in.
- I want to develop my own algorithmic trading strategy. Can I use a Quantra course notebook for the same?
Quantra environment is a zero-installation solution to get beginners to start off with coding in Python. While learning you won't have to download or install anything! However, if you wish to later implement the learning on your system, you can definitely do that. All the notebooks in the Quantra portal are available for download at the end of each course and they can be run in the local system just the same as they run in the portal. The user can modify/tweak/rework all such code files as per his need. We encourage you to implement different concepts learnt from different learning tracks into your trading strategy to make it more suited to the real-world scenario.
- If I plug in the Quantra code to my trading system, am I sure to make money?
No. We provide you guidance on how to create strategy using different techniques and indicators, but no strategy is plug and play. A lot of effort is required to backtest any strategy, after which we fine-tune the strategy parameters and see the performance on paper trading before we finally implement the live execution of trades.
- What does "lifetime access" mean?
Lifetime access means that once you enroll in the course, you will have unlimited access to all course materials, including videos, resources, readings, and other learning materials for as long as the course remains available online. There are no time limits or expiration dates on your access, allowing you to learn at your own pace and revisit the content whenever you need it, even after you've completed the course. It's important to note that "lifetime" refers to the lifetime of the course itself—if the platform or course is discontinued for any reason, we will inform you in advance. This will allow you enough time to download or access any course materials you need for future use.
- How is machine learning used in finance?
Machine learning analyses a large number of datasets to accomplish a specific task without being explicitly programmed. In finance, machine learning has a wide array of applications.
-
Price movement prediction: Machine learning models use security's prices, monitor the news, and detect patterns that can help predict the securities' price movements.
-
Portfolio and risk management: Machine learning algorithms analyse the trade results in real-time and help to manage risks. It also manages and creates financial portfolios according to the goals and risks of the traders.
-
Fraud detection: Fraud is one of the major problems in banking and financial institutions. Machine learning scans large datasets to detect anomalies and flags them for further investigations.
-
Loan underwriting: Machine learning algorithms quickly analyse consumer data, such as age, income, consumer's credit behaviour etc. and make decisions on underwriting and credit scoring of individuals.
-
- What are the applications of machine learning?
Machine learning has various applications in our day to day life. The task can be as simple as recognizing human handwriting or as complex as self-driving cars.
For example, one of the common tasks of machine learning is face recognition. Facebook uses face and image detection to tag the person’s face with their name automatically.
Another typical example is product recommendation. When you search for an item on Google, you start getting similar product recommendations on various websites.
Other popular applications that everyone uses in their daily lives are speech recognition or speech to text recognition.
Due to the ability to solve complex mathematical problems, machine learning is widely used in the financial industry. It helps in predicting the market trend by going through a large set of datasets. The application ranges from portfolio management to fraud detection in financial institutions. - What are the types of machine learning?
Broadly, there are three types of machine learning algorithms: Supervised, unsupervised and reinforcement learning.
In supervised learning, the expected outcome is clearly defined. Each training data consists of input objects and the desired output or target value. The main task is to produce a function that will map input values to the output value. This way, the machine learns to predict an output value with any new input value.
The supervised learning problems can be further divided into classification and regression problems.- When the output is in classes such as buy or sell, it is a classification problem.
- Whereas, if the output is continuous, such as predicting the price of a stock, it is called a regression problem.
Unsupervised learning algorithms consist of input datasets without labelled responses. Hence the algorithm is forced to learn the correct way to produce an output in an unsupervised manner. One of the tasks of unsupervised learning is clustering. The goal of clustering is to find
similarities in the training data and cluster similar data points in one group.
In reinforcement learning, a model or agent makes a sequence of decisions in a complex environment to achieve a particular goal. The agent decides on the action and compares the outcome against a predefined reward system. The goal is to take action in such a manner to maximise the rewards. - What machine learning algorithms are used in finance?
The use of machine learning algorithms in finance depends on the task at hand. For example, if you want to predict the market trend, whether the price of a security will go up or down the next day, you can use classification algorithms.
Examples of classification algorithms are- Logistic Regression,
- KNN classification,
- Support Vector Machine, and
- Artificial Neural Networks.
However, if the task is to predict the security price the next day, you can use regression algorithms such as:
- Linear regression,
- Regression tree, and
- Random Forest.
The task of machine learning is not only limited to security price prediction. More complex algorithms like deep neural networks are also used in finance.
- Can Python be used for finance?
Yes, Python is widely used in finance and trading. Python is a free, open-source platform that has a rich library for almost every task imaginable. And is suitable for medium to low-frequency trading that is trading on a time scale of minutes and above.
Python contains powerful libraries that help in achieving most of the financial tasks.
- Pandas is an open-source library used for data analysis and manipulation.
- Numpy and SciPy are two powerful tools for scientific and technical computing. It contains modules for statistics, linear algebra, and calculus.
Plotting and representation are crucial parts of financial modelling. Matplotlib library helps to generate plots, histograms, bar charts, error charts, scatter plots, etc. - Can I predict the direction of movement of an asset’s price using machine learning?
The direction of the trend can be predicted using a classification model. For example, the decision tree models can be used to predict the direction of price movement for the next day.
Similarly, other models like hierarchical clustering and Support Vector Machines can be used to solve problems where a classification output is required. - Can I predict an asset’s price using machine learning?
The price of an asset can be predicted using a regression model. For example, the linear regression models can be used to predict the open price of an asset for the next trading day.
Similarly, the close, the high or the low price of the asset for the next day can be predicted using regression models. - Can I apply the machine learning models to the asset of my choice?
The machine learning models are not specific to the particular asset taught in Quantra courses. The machine learning models can be applied to different assets of your choice.
But it must be noted that the price patterns of various assets are different, and they may give good returns for different types of machine learning models. There is no single model that can capture the trend of all possible assets. - Can I build machine learning models on Quantra or do I need to set up Python in my machine?
All the code illustration on Quantra is done using the Jupyter notebooks. You can run most of the machine learning models right on the Quantra platform. You also exercise the code snippets in online interactive exercises.
Some of the heavier machine models, like neural networks and reinforcement learning models, can not run on Quantra because of memory limitations.
But don’t worry! All the code is available in a downloadable format and can be run on your local machine using Anaconda’s Jupyter notebook. You can set up your local environment by following the steps outlined in this article. - Can the learning from Quantra courses be applied on different broker platforms?
The quantitative principles learnt from Quantra are not specific to any broker and can be applied across different broker platforms. To make things simpler, we provide Blueshift or IBridgePy integration as they cover some of the most popular brokers for traders across the globe.
If you prefer to use your own broker, you can use the learnings from the Quantra courses and connect to your own broker’s API to perform the trades.