Algorithmic Trading AZ with Python Machine Learning AWS

Algorithmic Trading AZ with Python Machine Learning AWS

What you’ll learn

Build automated Trading Bots with Python and Amazon Web Services (AWS)
Create powerful and unique Trading Strategies based on Technical Indicators and Machine Learning / Deep Learning.
Rigorous Testing of Strategies: Backtesting, Forward Testing and live Testing with paper money.
Fully automate and schedule your Trades on a virtual Server in the AWS Cloud.
Truly Data-driven Trading and Investing.
Python Coding and Object Oriented Programming (OOP) in a way that everybody understands it.
Coding with Numpy, Pandas, Matplotlib, scikit-learn, Keras and Tensorflow.
Understand Day Trading A-Z: Spread, Pips, Margin, Leverage, Bid and Ask Price, Order Types, Charts & more.
Day Trading with Brokers OANDA, Interactive Brokers (IBKR) and FXCM.
Stream high-frequency real-time Data.
Understand, analyze, control and limit Trading Costs.
Use powerful Broker APIs and connect with Python.

Requirements

No prior Python knowledge required. This course provides a Python Crash Course.
No prior Finance/Trading knowledge required. This course explains the Basics.
A desktop computer (Windows, Mac, or Linux) capable of storing and running Anaconda. The course will walk you through installing the necessary free software.
An internet connection capable of streaming HD videos.
Some high school level math skills would be great (not mandatory, but it helps)

Description

Welcome to the most comprehensive Algorithmic Trading Course. It´s the first 100% Data-driven Trading Course!*** MARCH 2023:  Course fully updated and now with an additional Broker: Interactive Brokers (IBKR)***Did you know that 75% of retail Traders lose money with Day Trading? (some sources say >95%)For me as a Data Scientist and experienced Finance Professional this is not a surprise. Day Traders typically do not know/follow the five fundamental rules of (Day) Trading. This Course covers them all in detail!1. Know and understand the Day Trading BusinessDon´t start Trading if you are not familiar with terms like Bid-Ask Spread, Pips, Leverage, Margin Requirement, Half-Spread Costs, etc.Part 1 of this course is all about Day Trading A-Z with the Brokers Oanda, Interactive Brokers, and FXCM. It deeply explains the mechanics, terms, and rules of Day Trading (covering Forex, Stocks, Indices, Commodities, Baskets, and more).2. Use powerful and unique Trading StrategiesYou need to have a Trading Strategy. Intuition or gut feeling is not a successful strategy in the long run (at least in 99.9% of all cases). Relying on simple Technical Rules doesn´t work either because everyone uses them.You will learn how to develop more complex and unique Trading Strategies with Python. We will combine simple and also more complex Technical Indicators and we will also create Machine Learning- and Deep Learning- powered Strategies. The course covers all required coding skills (Python, Numpy, Pandas, Matplotlib, scikit-learn, Keras, Tensorflow) from scratch in a very practical manner. 3. Test your Strategies before you invest real money (Backtesting / Forward Testing)Is your Trading Strategy profitable? You should rigorously test your strategy before ‘going live’.This course is the most comprehensive and rigorous Backtesting / Forward Testing course that you can find.You will learn how to apply Vectorized Backtesting techniques, Iterative Backtesting techniques (event-driven), live Testing with play money, and more. And I will explain the difference between Backtesting and Forward Testing and show you what to use when. The backtesting techniques and frameworks covered in the course can be applied to long-term investment strategies as well!   4. Take into account Trading Costs – it´s all about Trading Costs!”Trading with zero commissions? Great!” … Well, there is still the Bid-Ask-Spread and even if 2 Pips seem to be very low, it isn´t! The course demonstrates that finding profitable Trading Strategies before Trading Costs is simple. It´s way more challenging to find profitable Strategies after Trading Costs! Learn how to include Trading Costs into your Strategy and into Strategy Backtesting / Forward Testing. And most important: Learn how you can control and reduce Trading Costs.  5. Automate your TradesManual Trading is error-prone, time-consuming, and leaves room for emotional decision-making. This course teaches how to implement and automate your Trading Strategies with Python, powerful Broker APIs, and Amazon Web Services (AWS). Create your own Trading Bot and fully automate/schedule your trading sessions in the AWS Cloud! Finally… this is more than just a course on automated Day Trading:the techniques and frameworks covered can be applied to long-term investing as well.it´s an in-depth Python Course that goes beyond what you can typically see in other courses. Create Software with Python and run it in real-time on a virtual Server (AWS)!we will feed Machine Learning & Deep Learning Algorithms with real-time data and take ML/DL-based actions in real-time! What are you waiting for? Join now. As always, there is no risk for you as I provide a 30-Days-Money-Back Guarantee!Thanks and looking forward to seeing you in the Course!

Overview

Section 1: Getting Started

Lecture 1 What is Algorithmic Trading / Course Overview

Lecture 2 How to get the best out of this course

Lecture 3 Did you know…? (what Data can tell us about Day Trading)

Lecture 4 Student FAQ

Lecture 5 *** LEGAL DISCLAIMER (MUST READ!) ***

Section 2: +++ PART 1: Day Trading, Online Brokers and APIs +++

Lecture 6 Our very first Trade

Lecture 7 Long Term Investing vs. (Algorithmic) Day Trading

Lecture 8 Spot Trading vs. Derivatives Trading (Part 1)

Lecture 9 Spot Trading vs. Derivatives Trading (Part 2)

Lecture 10 Overview & the Brokers OANDA, IBKR and FXCM

Section 3: Day Trading with OANDA A-Z: a Deep Dive

Lecture 11 OANDA at a first glance

Lecture 12 How to create an Account

Lecture 13 FOREX / Currency Exchange Rates explained

Lecture 14 Our second Trade – EUR/USD FOREX Trading

Lecture 15 How to calculate Profit & Loss of a Trade

Lecture 16 Trading Costs and Performance Attribution

Lecture 17 Margin and Leverage

Lecture 18 Margin Closeout and more

Lecture 19 Introduction to Charting

Lecture 20 Our third Trade A-Z – Going Short EUR/USD

Lecture 21 Netting vs. Hedging

Lecture 22 Market, Limit and Stop Orders

Lecture 23 Take-Profit and Stop-Loss Orders

Lecture 24 A more general Example

Lecture 25 Trading Challenge

Section 4: Stocks and FOREX Trading with Interactive Brokers (IBKR)

Lecture 26 IBKR at a first glance

Lecture 27 How to create a (Paper Trading) Account

Lecture 28 How to Install the IB Trader Workstation (TWS)

Lecture 29 TWS – First Steps

Lecture 30 The first Trade (buying Stocks)

Lecture 31 Trading Hours

Lecture 32 Cash Account vs. Margin Account

Lecture 33 Trading Costs (Stocks) – Commissions

Lecture 34 Trading Costs (Stocks) – other (hidden) Costs

Lecture 35 FOREX Trading: Cash vs. CFD

Lecture 36 A complete CFD FOREX Trade

Lecture 37 CFD Trade Analysis

Section 5: FOREX Day Trading with FXCM

Lecture 38 FXCM at a first glance

Lecture 39 How to create an Account

Lecture 40 Example Trade: Buying EUR/USD

Lecture 41 Trade Analysis

Lecture 42 Charting

Lecture 43 Closing Positions vs. Hedging Positions

Lecture 44 Order Types at a glance

Lecture 45 Trading Challenge

Section 6: Installing Python and Jupyter Notebooks

Lecture 46 Introduction

Lecture 47 Download and Install Anaconda

Lecture 48 How to open Jupyter Notebooks

Lecture 49 How to work with Jupyter Notebooks

Lecture 50 Tips for Python Beginners

Section 7: Excursus: How to avoid and debug Coding Errors (don´t skip!)

Lecture 51 Introduction

Lecture 52 Test your debugging skills!

Lecture 53 Major reasons for Coding Errors

Lecture 54 The most commonly made Errors at a glance

Lecture 55 Omitting cells, changing the sequence and more

Lecture 56 IndexErrors

Lecture 57 Indentation Errors

Lecture 58 Misuse of function names and keywords

Lecture 59 TypeErrors and ValueErrors

Lecture 60 Getting help on StackOverflow.com

Lecture 61 How to traceback more complex Errors

Lecture 62 Problems with the Python Installation

Lecture 63 External Factors and Issues

Lecture 64 Errors related to the course content (Transcription Errors)

Lecture 65 Summary and Debugging Flow-Chart

Section 8: API Trading with Python and Online Brokers- an Introduction

Lecture 66 Overview

Lecture 67 OANDA: Commands to install required packages

Lecture 68 OANDA: How to install the OANDA API / Wrapper

Lecture 69 OANDA: Getting the API Key & other Preparations

Lecture 70 OANDA: Connecting to the API/Server

Lecture 71 OANDA: How to load Historical Price Data (Part 1)

Lecture 72 OANDA: How to load Historical Price Data (Part 2)

Lecture 73 OANDA: Streaming high-frequency real-time Data

Lecture 74 OANDA: How to place Orders and execute Trades

Lecture 75 Trading Challenge

Lecture 76 IBKR API: Downloads and required Commands to install the Wrapper

Lecture 77 IBKR: How to download and install the API Wrapper & other Preparations

Lecture 78 IBKR: Connecting to the API

Lecture 79 IBKR: Contracts

Lecture 80 IBKR: How to get Market Data

Lecture 81 IBKR: Data Streaming for Multiple Tickers

Lecture 82 IBKR: Contracts (advanced)

Lecture 83 IBKR: FOREX and CFD Contracts

Lecture 84 IBKR: Creating Orders (Stock Trading)

Lecture 85 IBKR: Creating Orders (CFD Trading)

Lecture 86 IBKR: CFD Trade Information

Lecture 87 IBKR: Positions and Account Values

Lecture 88 IBKR: Historical Bars

Lecture 89 FXCM: Commands to install required packages

Lecture 90 FXCM: How to install the FXCM API Wrapper

Lecture 91 FXCM: Getting the Access Token & other Preparations

Lecture 92 FXCM: Connecting to the API/Server

Lecture 93 Troubleshooting: FXCM Server Connection Issues

Lecture 94 FXCM: How to load Historical Price Data (Part 1)

Lecture 95 FXCM: How to load Historical Price Data (Part 2)

Lecture 96 FXCM: Streaming high-frequency real-time Data

Lecture 97 FXCM: How to place Orders and execute Trades

Lecture 98 Trading Challenge

Section 9: Conclusion and Outlook

Lecture 99 Conclusion and Outlook

Section 10: +++ PART 2: Pandas for Financial Data Analysis and Introduction to OOP +++

Lecture 100 Introduction and Downloads Part 2 ***Updated March 2023***

Section 11: Introduction to Time Series Data in Pandas

Lecture 101 Importing Time Series Data from csv-files

Lecture 102 Converting strings to datetime objects with pd.to_datetime()

Lecture 103 Indexing and Slicing Time Series

Lecture 104 Downsampling Time Series with resample()

Lecture 105 Coding Exercise 1

Section 12: Financial Data Analysis with Python and Pandas – a (deep) Introduction

Lecture 106 Introduction and Overview

Lecture 107 Installing and importing required Libraries/Packages

Lecture 108 Loading Financial Data from the Web

Lecture 109 Initial Inspection and Visualization

Lecture 110 [Article] Loading Data into Pandas – advanced topics

Lecture 111 Normalizing Time Series to a Base Value (100)

Lecture 112 Coding Challenge #1

Lecture 113 Price changes and Financial Returns

Lecture 114 Reward and Risk of Financial Instruments

Lecture 115 Coding Challenge #2

Lecture 116 Investment Multiple and CAGR

Lecture 117 Compound Returns & Geometric Mean Return

Lecture 118 Coding Challenge #3

Lecture 119 Discrete Compounding

Lecture 120 Continuous Compounding

Lecture 121 Log Returns

Lecture 122 Simple Returns vs Log Returns ( Part 1)

Lecture 123 Simple Returns vs Log Returns ( Part 2)

Lecture 124 Coding Challenge #4

Lecture 125 Comparing the Performance of Financial Instruments

Lecture 126 (Non-) Normality of Financial Returns

Lecture 127 Annualizing Return and Risk

Lecture 128 Resampling / Smoothing of Financial Data

Lecture 129 Rolling Statistics

Lecture 130 Coding Challenge #5

Lecture 131 Short Selling and Short Position Returns (Part 1)

Lecture 132 Short Selling and Short Position Returns (Part 2)

Lecture 133 Short Selling and Short Position Returns (Part 3)

Lecture 134 Coding Challenge #6

Lecture 135 Covariance and Correlation

Lecture 136 Portfolios and Portfolio Returns

Lecture 137 Margin Trading and Levered Returns (Part 1)

Lecture 138 Margin Trading and Levered Returns (Part 2)

Lecture 139 Coding Challenge #7

Section 13: Advanced Topics

Lecture 140 Importing Financial Data from Excel

Lecture 141 Merging / Aligning Financial Time Series (hands-on)

Lecture 142 Helpful DatetimeIndex Attributes and Methods

Lecture 143 Filling NA Values with bfill, ffill and interpolation

Lecture 144 Timezones and Converting (Part 1)

Lecture 145 Timezones and Converting (Part 2)

Section 14: Object Oriented Programming (OOP): Creating a Financial Analysis Class

Lecture 146 Introduction to OOP and examples for Classes

Lecture 147 The Financial Analysis Class live in action (Part 1)

Lecture 148 The Financial Analysis Class live in action (Part 2)

Lecture 149 The special method __init__()

Lecture 150 The method get_data()

Lecture 151 The method log_returns()

Lecture 152 String representation and the special method __repr__()

Lecture 153 The methods plot_prices() and plot_returns()

Lecture 154 Encapsulation and protected Attributes

Lecture 155 The method set_ticker()

Lecture 156 Adding more methods and performance metrics

Lecture 157 Inheritance

Lecture 158 Inheritance and the super() Function

Lecture 159 Adding meaningful Docstrings

Lecture 160 Creating and Importing Python Modules (.py)

Lecture 161 Coding Exercise 3: Create your own Class

Section 15: +++ PART 3: Defining and Testing Trading Strategies +++

Lecture 162 Introduction to Part 3

Lecture 163 Trading Strategies – an Overview

Lecture 164 Downloads for Part 3

Lecture 165 Getting the Data

Lecture 166 A simple Buy and Hold “Strategy”

Lecture 167 Performance Metrics

Section 16: Defining and Backtesting SMA Strategies

Lecture 168 SMA Crossover Strategies – Overview

Lecture 169 Defining an SMA Crossover Strategy

Lecture 170 Vectorized Strategy Backtesting

Lecture 171 Finding the optimal SMA Strategy

Lecture 172 Generalization with OOP: An SMA Backtesting Class in action

Lecture 173 Creating the Class (Part 1)

Lecture 174 Creating the Class (Part 2)

Lecture 175 Creating the Class (Part 3)

Lecture 176 Creating the Class (Part 4)

Lecture 177 Creating the Class (Part 5)

Lecture 178 Creating the Class (Part 6)

Lecture 179 Creating the Class (Part 7)

Lecture 180 Creating the Class (Part 8)

Section 17: Defining and Backtesting simple Momentum/Contrarian Strategies

Lecture 181 Simple Contrarian/Momentum Strategies – Overview

Lecture 182 Getting the Data

Lecture 183 Excursus: Your FAQs answered

Lecture 184 Defining a simple Contrarian Strategy

Lecture 185 Vectorized Strategy Backtesting

Lecture 186 Changing the Window Parameter

Lecture 187 Trades and Trading Costs (Part 1)

Lecture 188 Trades and Trading Costs (Part 2)

Lecture 189 Generalization with OOP: A Contrarian Backtesting Class in action

Lecture 190 OOP Challenge: Create the Contrarian Backtesting Class (incl. Solution)

Section 18: Defining and Backtesting Mean-Reversion Strategies (Bollinger)

Lecture 191 Mean-Reversion Strategies – Overview

Lecture 192 Getting the Data

Lecture 193 Defining a Bollinger Bands Mean-Reversion Strategy (Part 1)

Lecture 194 Defining a Bollinger Bands Mean-Reversion Strategy (Part 2)

Lecture 195 Vectorized Strategy Backtesting

Lecture 196 Generalization with OOP: A Bollinger Bands Backtesting Class in action

Lecture 197 OOP Challenge: Create the Bollinger Bands Backtesting Class (incl. Solution)

Section 19: Trading Strategies powered by Machine Learning – Regression

Lecture 198 Machine Learning – an Overview

Lecture 199 Linear Regression with scikit-learn – a simple Introduction

Lecture 200 Making Predictions with Linear Regression

Lecture 201 Overfitting

Lecture 202 Underfitting

Lecture 203 Getting the Data

Lecture 204 A simple Linear Model to predict Financial Returns (Part 1)

Lecture 205 A simple Linear Model to predict Financial Returns (Part 2)

Lecture 206 A Multiple Regression Model to predict Financial Returns

Lecture 207 In-Sample Backtesting and the Look-ahead-bias

Lecture 208 Out-Sample Forward Testing

Section 20: Trading Strategies powered by Machine Learning – Classification

Lecture 209 Logistic Regression with scikit-learn – a simple Introduction (Part 1)

Lecture 210 Logistic Regression with scikit-learn – a simple Introduction (Part 2)

Lecture 211 Getting and Preparing the Data

Lecture 212 Predicting Market Direction with Logistic Regression

Lecture 213 In-Sample Backtesting and the Look-ahead-bias

Lecture 214 Out-Sample Forward Testing

Lecture 215 Generalization with OOP: A Classification Backtesting Class in action

Lecture 216 The Classification Backtesting Class explained (Part 1)

Lecture 217 The Classification Backtesting Class explained (Part 2)

Section 21: Advanced Backtesting Techniques

Lecture 218 Introduction to Iterative Backtesting (“event-driven”)

Lecture 219 A first Intuition on Iterative Backtesting (Part 1)

Lecture 220 A first Intuition on Iterative Backtesting (Part 2)

Lecture 221 Creating an Iterative Base Class (Part 1)

Lecture 222 Creating an Iterative Base Class (Part 2)

Lecture 223 Creating an Iterative Base Class (Part 3)

Lecture 224 Creating an Iterative Base Class (Part 4)

Lecture 225 Creating an Iterative Base Class (Part 5)

Lecture 226 Creating an Iterative Base Class (Part 6)

Lecture 227 Creating an Iterative Base Class (Part 7)

Lecture 228 Creating an Iterative Base Class (Part 8)

Lecture 229 Adding the Iterative Backtest Child Class for SMA (Part 1)

Lecture 230 Adding the Iterative Backtest Child Class for SMA (Part 2)

Lecture 231 Using Modules and adding Docstrings

Lecture 232 OOP Challenge: Add Contrarian and Bollinger Strategies

Section 22: +++ PART 4: Real-time Implementation and Automation of Strategies +++

Lecture 233 Introduction and Overview

Lecture 234 Downloads for Part 4 *** Updated March 2023 ***

Section 23: Implementation and Automation with OANDA (UPDATED!)

Lecture 235 Updating the Wrapper Package (Part 1)

Lecture 236 Updating the Wrapper Package (Part 2)

Lecture 237 **Weekend and Bank Holiday Alert**

Lecture 238 Historical Data, real-time Data and Orders (Recap)

Lecture 239 Preview: A Trader Class live in action

Lecture 240 How to collect and store real-time tick data

Lecture 241 Storing and resampling real-time tick data (Part 1)

Lecture 242 Storing and resampling real-time tick data (Part 2)

Lecture 243 Storing and resampling real-time tick data (Part 3)

Lecture 244 Storing and resampling real-time tick data (Part 4)

Lecture 245 Storing and resampling real-time tick data (Part 5)

Lecture 246 Working with historical data and real-time tick data (Part 1)

Lecture 247 Working with historical data and real-time tick data (Part 2)

Lecture 248 Working with historical data and real-time tick data (Part 3)

Lecture 249 Defining a simple Contrarian Strategy

Lecture 250 Placing Orders and Executing Trades

Lecture 251 Trade Monitoring and Reporting

Lecture 252 Trading other Strategies – Coding Challenge

Lecture 253 Implementing an SMA Crossover Strategy (Solution)

Lecture 254 Implementing a Bollinger Bands Strategy (Solution)

Lecture 255 Machine Learning Strategies (1) – Model Fitting

Lecture 256 Machine Learning Strategies (2) – Implementation

Lecture 257 Importing a Trader Module / Class

Lecture 258 Excursus: Printing all ticks in a Command Prompt/Terminal

Lecture 259 Running a Python Trader Script

Section 24: Implementation and Automation with IBKR

Lecture 260 IBKR API – Recap

Lecture 261 Streaming Tick Data

Lecture 262 Streaming Tick Data for multiple Symbols

Lecture 263 Streaming Bar Data

Lecture 264 How to create a live Candle Stick Chart

Lecture 265 Preparing the Data for Day Trading

Lecture 266 Improving Code Efficiency

Lecture 267 Define an SMA Day Trading Strategy

Lecture 268 Creating Orders and Executing Trades

Lecture 269 Trade Monitoring and Reporting

Lecture 270 How to Stop a Trading Session

Lecture 271 Trading other Strategies – Coding Challenge

Lecture 272 Running a Python Trader Script

Section 25: Implementation and Automation with FXCM (Updated!)

Lecture 273 **Weekend and Bank Holiday Alert**

Lecture 274 Historical Data, real-time Data and Orders (Recap)

Lecture 275 Troubleshooting: FXCM Server Connection Issues

Lecture 276 Preview: A Trader Class live in action

Lecture 277 Collecting and storing real-time tick data

Lecture 278 Storing and resampling real-time tick data (Part 1)

Lecture 279 A Trader Class

Lecture 280 Storing and resampling real-time tick data (Part 2)

Lecture 281 Storing and resampling real-time tick data (Part 3)

Lecture 282 Working with historical data and real-time tick data (Part 1)

Lecture 283 Working with historical data and real-time tick data (Part 2)

Lecture 284 Working with historical data and real-time tick data (Part 3)

Lecture 285 Defining a Simple Contrarian Trading Strategy

Lecture 286 Placing Orders and Executing Trades

Lecture 287 Trade Monitoring and Reporting

Lecture 288 Trading other Strategies – Coding Challenge

Lecture 289 SMA Crossover and Bollinger Bands (Solution)

Lecture 290 Machine Learning Strategies (1) – Model Fitting

Lecture 291 Machine Learning Strategies (2) – Implementation

Lecture 292 Excursus: Printing all ticks in a Command Prompt/Terminal

Lecture 293 Running a Python Script

Section 26: Cloud Deployment (AWS) | Scheduling Trading Sessions | Full Automation

Lecture 294 Introduction and Motivation

Lecture 295 Demonstration: AWS EC2 for Algorithmic Trading live in action

Lecture 296 Amazon Web Services (AWS) – Overview and how to create a Free Trial Account

Lecture 297 How to create an EC2 Instance

Lecture 298 How to connect to your EC2 Instance

Lecture 299 Getting the Instance Ready for Algorithmic Trading

Lecture 300 **Weekend and Bank Holiday Alert**

Lecture 301 How to run Python Scripts in a Windows Command Prompt

Lecture 302 How to start Trading sessions with Batch (.bat) Files

Lecture 303 How to schedule Trading sessions with the Task Scheduler

Lecture 304 How to stop Trading Sessions (OANDA)

Lecture 305 How to stop Trading Sessions (FXCM)

Section 27: +++ PART 5: Expert Tips & Tricks, Case Studies and more +++

Lecture 306 Overview

Lecture 307 Downloads for PART 5

Section 28: Trading Hours, Spreads and Granularity – control and limit Trading Costs!

Lecture 308 Introduction and Preparing the Data

Lecture 309 The best time to trade (Part 1)

Lecture 310 The best time to trade (Part 2)

Lecture 311 Spreads during the busy hours

Lecture 312 The Impact of Granularity

Lecture 313 Conclusions

Section 29: Working with two or many Strategies (Combination)

Lecture 314 Introduction

Lecture 315 Strategy 1: SMA

Lecture 316 Strategy 2: Mean Reversion

Lecture 317 Combining both Strategies – Alternative 1

Lecture 318 Taking into account busy Trading Hours

Lecture 319 Strategy Backtesting

Lecture 320 Combining both Strategies – Alternative 2

Lecture 321 Strategy Optimization

Section 30: A Machine Learning-powered Strategy A-Z (DNN)

Lecture 322 Project Overview

Lecture 323 Installation of Tensorflow & Keras (Part 1)

Lecture 324 Installation of Tensorflow & Keras (Part 2)

Lecture 325 Getting and Preparing the Data

Lecture 326 Adding Labels/Features

Lecture 327 Adding lags

Lecture 328 Splitting into Train and Test Set

Lecture 329 Feature Scaling/Engineering

Lecture 330 Creating and Fitting the DNN Model

Lecture 331 Prediction & Out-Sample Forward Testing

Lecture 332 Saving Model and Parameters

Lecture 333 **Important Notices**

Lecture 334 Implementation (Oanda & FXCM)

Section 31: Error Handling: How to make your Trading Bot more stable and reliable

Lecture 335 Introduction

Lecture 336 Section Materials / Notebooks

Lecture 337 Python Errors (Exceptions)

Lecture 338 try and except

Lecture 339 Catching specific Errors

Lecture 340 The Exception class

Lecture 341 try, except, else

Lecture 342 finally

Lecture 343 Try again (…until it works)

Lecture 344 How to limit the number of retries

Lecture 345 Waiting periods between re-tries

Lecture 346 Implementation with Oanda: V20 Connection Issues

Lecture 347 Oanda Error Handling (Part 1)

Lecture 348 Oanda Error Handling (Part 2)

Lecture 349 Oanda Error Handling (Part 3)

Lecture 350 Implementation with FXCM: API/Server Issues

Lecture 351 FXCM Error Handling (Part 1)

Lecture 352 FXCM Error Handling (Part 2)

Section 32: +++ APPENDIX: Python Crash Course +++

Lecture 353 Overview

Section 33: Appendix 1: Python (& Finance) Basics

Lecture 354 Section Downloads

Lecture 355 Intro to the Time Value of Money (TVM) Concept (Theory)

Lecture 356 Calculate Future Values (FV) with Python / Compounding

Lecture 357 ***NEW*** Udemy Online Coding Exercises – Intro

Lecture 358 Calculate Present Values (PV) with Python / Discounting

Lecture 359 Interest Rates and Returns (Theory)

Lecture 360 Calculate Interest Rates and Returns with Python

Lecture 361 Introduction to Variables

Lecture 362 Excursus: How to add inline comments

Lecture 363 Variables and Memory (Theory)

Lecture 364 More on Variables and Memory

Lecture 365 Variables – Dos, Don´ts and Conventions

Lecture 366 The print() Function

Lecture 367 Coding Exercise 1

Lecture 368 TVM Problems with many Cashflows

Lecture 369 Intro to Python Lists

Lecture 370 Zero-based Indexing and negative Indexing in Python (Theory)

Lecture 371 Indexing Lists

Lecture 372 For Loops – Iterating over Lists

Lecture 373 The range Object – another Iterable

Lecture 374 Calculate FV and PV for many Cashflows

Lecture 375 The Net Present Value – NPV (Theory)

Lecture 376 Calculate an Investment Project´s NPV

Lecture 377 Coding Exercise 2

Lecture 378 Data Types in Action

Lecture 379 The Data Type Hierarchy (Theory)

Lecture 380 Excursus: Dynamic Typing in Python

Lecture 381 Build-in Functions

Lecture 382 Integers

Lecture 383 Floats

Lecture 384 How to round Floats (and Integers) with round()

Lecture 385 More on Lists

Lecture 386 Lists and Element-wise Operations

Lecture 387 Slicing Lists

Lecture 388 Slicing Cheat Sheet

Lecture 389 Changing Elements in Lists

Lecture 390 Sorting and Reversing Lists

Lecture 391 Adding and removing Elements from/to Lists

Lecture 392 Mutable vs. immutable Objects (Part 1)

Lecture 393 Mutable vs. immutable Objects (Part 2)

Lecture 394 Coding Exercise 3

Lecture 395 Tuples

Lecture 396 Dictionaries

Lecture 397 Intro to Strings

Lecture 398 String Replacement

Lecture 399 Booleans

Lecture 400 Operators (Theory)

Lecture 401 Comparison, Logical and Membership Operators in Action

Lecture 402 Coding Exercise 4

Lecture 403 Conditional Statements

Lecture 404 Keywords pass, continue and break

Lecture 405 Calculate a Project´s Payback Period

Lecture 406 Introduction to while loops

Lecture 407 Coding Exercise 5

Section 34: Appendix 2: User-defined Functions (required for OOP)

Lecture 408 Section Downloads

Lecture 409 Defining your first user-defined Function

Lecture 410 What´s the difference between Positional Arguments vs. Keyword Arguments?

Lecture 411 How to work with Default Arguments

Lecture 412 The Default Argument None

Lecture 413 How to unpack Iterables

Lecture 414 Sequences as arguments and *args

Lecture 415 How to return many results

Lecture 416 Scope – easily explained

Lecture 417 Coding Exercise 6

Section 35: Appendix 3: Numpy, Pandas, Matplotlib and Seaborn Crash Course

Lecture 418 Downloads for this Section

Lecture 419 Modules, Packages and Libraries – No need to reinvent the Wheel

Lecture 420 Numpy Arrays

Lecture 421 Indexing and Slicing Numpy Arrays

Lecture 422 Vectorized Operations with Numpy Arrays

Lecture 423 Changing Elements in Numpy Arrays & Mutability

Lecture 424 View vs. copy – potential Pitfalls when slicing Numpy Arrays

Lecture 425 Numpy Array Methods and Attributes

Lecture 426 Numpy Universal Functions

Lecture 427 Boolean Arrays and Conditional Filtering

Lecture 428 Advanced Filtering & Bitwise Operators

Lecture 429 Determining a Project´s Payback Period with np.where()

Lecture 430 Creating Numpy Arrays from Scratch

Lecture 431 Coding Exercise 7

Lecture 432 How to work with nested Lists

Lecture 433 2-dimensional Numpy Arrays

Lecture 434 How to slice 2-dim Numpy Arrays (Part 1)

Lecture 435 How to slice 2-dim Numpy Arrays (Part 2)

Lecture 436 Recap: Changing Elements in a Numpy Array / slice

Lecture 437 How to perform row-wise and column-wise Operations

Lecture 438 Coding Exercise 8

Lecture 439 Intro to Tabular Data / Pandas

Lecture 440 Create your very first Pandas DataFrame (from csv)

Lecture 441 Pandas Display Options and the methods head() & tail()

Lecture 442 First Data Inspection

Lecture 443 Coding Exercise 9

Lecture 444 Selecting Columns

Lecture 445 Selecting one Column with the “dot notation”

Lecture 446 Zero-based Indexing and Negative Indexing

Lecture 447 Selecting Rows with iloc (position-based indexing)

Lecture 448 Slicing Rows and Columns with iloc (position-based indexing)

Lecture 449 Position-based Indexing Cheat Sheets

Lecture 450 Selecting Rows with loc (label-based indexing)

Lecture 451 Slicing Rows and Columns with loc (label-based indexing)

Lecture 452 Label-based Indexing Cheat Sheets

Lecture 453 Summary, Best Practices and Outlook

Lecture 454 Coding Exercise 10

Lecture 455 First Steps with Pandas Series

Lecture 456 Analyzing Numerical Series with unique(), nunique() and value_counts()

Lecture 457 Analyzing non-numerical Series with unique(), nunique(), value_counts()

Lecture 458 The copy() method

Lecture 459 Sorting of Series and Introduction to the inplace – parameter

Lecture 460 First Steps with Pandas Index Objects

Lecture 461 Changing Row Index with set_index() and reset_index()

Lecture 462 Changing Column Labels

Lecture 463 Renaming Index & Column Labels with rename()

Lecture 464 Filtering DataFrames (one Condition)

Lecture 465 Filtering DataFrames by many Conditions (AND)

Lecture 466 Filtering DataFrames by many Conditions (OR)

Lecture 467 Advanced Filtering with between(), isin() and ~

Lecture 468 Intro to NA Values / missing Values

Lecture 469 Handling NA Values / missing Values

Lecture 470 Exporting DataFrames to csv

Lecture 471 Summary Statistics and Accumulations

Lecture 472 Visualization with Matplotlib (Intro)

Lecture 473 Customization of Plots

Lecture 474 Histogramms (Part 1)

Lecture 475 Histogramms (Part 2)

Lecture 476 Scatterplots

Lecture 477 First Steps with Seaborn

Lecture 478 Categorical Seaborn Plots

Lecture 479 Seaborn Regression Plots

Lecture 480 Seaborn Heatmaps

Lecture 481 Removing Columns

Lecture 482 Introduction to GroupBy Operations

Lecture 483 Understanding the GroupBy Object

Lecture 484 Splitting with many Keys

Lecture 485 split-apply-combine

Section 36: What´s next? (outlook and additional resources)

Lecture 486 Bonus Lecture

(Day) Traders and Investors who want to professionalize and automate their Business.,(Day) Traders and Investors tired of relying on simple strategies, chance and hope.,Finance & Investment Professionals who want to step into Data-driven and AI-driven Finance.,Data Scientists and Machine Learning Professionals.

Course Information:

Udemy | English | 41h 35m | 16.79 GB
Created by: Alexander Hagmann

You Can See More Courses in the Business >> Greetings from CourseDown.com