Algorithmic Trading with Python

Introduction to Algorithm Trading using Python

• Technology transforms finance, enabling institutions to shift focus towards tech rather than just financial factors.
• Tech drives innovation, speeds things up, and enhances competitive speed.
• Programming languages for finance are R, Python, C++, C#, and Java.
• The course is about systematic use of algorithmic trading strategies in Python.
• Areas include using Pandas/NumPy for derivatives data analysis, back-testing models, and process automation via trading APIs.
• Core concepts: strategy coding, parameter optimization, and managing electronic-trading constraints like latency and slippage.

Course Objectives

• Pandas facilitates time-series manipulation for financial datasets by focusing on critical operations like rolling window computations (e.g., 30-day moving averages) and volatility metrics calculated from price returns.

Upon completion of this course, one would gain the ability to:

• Understand the fundamentals of stocks, trading strategies, time series data, and workspace setup.
• Implement various analyses through Series.resample() and DataFrame.rolling() methods, transforming raw historical data into strategy-relevant indicators.
• Practical applications: calculating annualized volatility using the standard deviation of logarithmic returns and identifying trend patterns through windowed aggregation.
• Develop a basic momentum strategy using a step-by-step process to formulate and code a simple algorithmic trading strategy.
• Back-test a developed trading strategy using Pandas, Zipline, and Quantopian.
• Discover methods to optimize a strategy for improved performance and to assess its effectiveness.

Basic Concepts

• Corporations raise expansion capital through equity shares (stocks).
• Secondary markets facilitate share trading, with prices set by supply-demand dynamics.
• Share valuations reflect aggregated investor sentiment/liquidity, not real-time operational metrics.
• A stock's price increases when it is considered desirable because of factors like success and popularity.
• Trading involves buying or selling assets, such as financial securities, like stocks or bonds.
• Stock trading means converting cash into company ownership shares, and converting them back for profit.
• To achieve return, one can go long (buy) or short (sell) in the markets.
• Algorithmic strategies systematically establish directional market exposure through predefined quantitative criteria.
• Frameworks codify entry/exit signals, position sizing, and risk parameters, often using statistical models or technical indicators.
• Strategies in derivatives include targeting arbitrage opportunities, volatility patterns, or hedging ratios across correlated instruments.
• Developing strategies has phases like building ML models, formulating a strategy, testing, optimizing, and evaluating robustness.
• Trading strategies are validated through back-testing, and that involves simulating past trades using historical data based on the strategy's defined rules.
• The success of a strategy back in the day is believed to indicate its future performance.

Time Series Data

• A time series comprises numerical data points recorded at regular intervals over time.
• The graph dates are marked on the x-axis, while prices are displayed on the y-axis.
• The phrase "successive equally spaced points in time" means uniform temporal intervals (e.g., 14-day gaps) exist in time-series datasets.
• In building trading models, data ensures alignment of historical data with strategy logic, such as back-testing a 14-day moving average crossover rule.
• Financial time-series datasets like OHLC (Open-High-Low-Close) have four price metrics: initial transaction price (Open); intra-period maximum (High); minimum (Low); final transaction price (Close).

Install "yfinance" Package

• The yfinance package enables retrieval of data from YahooFinance.
• Use pip to install the latest release of the package: pip install yfinance.
• The yfinance package can be imported in Python.
• Securities are identified via ticker symbols.
• For Amazon, the ticker symbol is "AMZN"; for Meta, it's "META"; for Google, it's "GOOGL".

Getting Financial Data from Yahoo Finance

• Financial information can be retrieved using import yfinance as yahooFinance followed by GetFacebookInformation = yahooFinance.Ticker ("META")
• One can quickly obtain metrics such as Company Sector, Price Earnings Ratio, and Company Beta.
• Even thought the financial metrics are in dictionary format, they can be separated into key-value pairs.

Getting Historical Data

• Historical market prices can be fetched and displayed
• The ticker symbol can be displayed by adjusting the set_option () function.
• We can access the data for different time frames, such as 1d, 5d, 1-10y, and year-to-date.
• Historical market data can be assessed for specified start and end dates using the datetime package.
• Withdrawing data is done using the csv () function.
• Parameters like index_col=0 and parse_dates=True are used to format timestamps for analysis.

Calculate Moving Average

• There are several approaches to computing the moving average in a Pandas DataFrame with three primary techniques to do so: Simple Moving Average (SMA), Exponential Moving Average (EMA), and Cumulative Moving Average (CMA)

Simple Moving Average (SMA)

• A Simple Moving Average (SMA) helps make calculations on a rolling window
• The mean() function to calculate mean of each window

Library and Data Import

• Code snippet imports the Pandas ('pd') and NumPy ('np') libraries and Matplotlib's pyplot module (' plt') and inlines the plots in Jupyter notebooks.
• The pandas.read_csv() function imports the data.

Extract Columns in DataFrame

• Code refines the 'meta' DataFrame by isolating the 'Close' column.
• It also calculates a 30-day SMA and displays the refined result.

Plotting Simple Moving Averages

• The plot() function shows the Close price and a 30-day Simple Moving Average (SMA) of the 'meta' DataFrame.

Cumulative Moving Average (CMA)

• Computing the Cumulative Moving Average utilizes the dataframe's expanding() function.
• The window size is not fixed, and it expands over time.

Extract Columns in DataFrame

• Code modifies the 'reliance' DataFrame to focus solely on the 'Close' column, transforming it into a new DataFrame.
• It calculates CMA using a window size of 30 for the 'Close' column
• Shows the CMA values

Plotting Cumulative Moving Averages

• The plot () approach is used to show the closing price and 30-day Cumulative Moving Average (CMA30) of Meta stock, with a designated label and figure size.

Exponential Moving Average (EMA)

• Calculating the Exponential Moving average utilizes the ewm () function.

Step 3: Extract Columns in a DataFrame

• Code extracts and isolates 'Close', converting the column to DataFrame.
• Exponential Moving Average (EWMA) is calculated with a span of 30 days
• Prints the DataFrame

Step 4: Plotting Exponential Moving Averages

• The plot() approach is utilizes to show the closing price and 30-day Exponential Moving Average (EWMA30)

What defines a trend in a time series?

• The financial time series is made of chronologically ordered observations of market variables (e.g., derivatives prices, implied volatility recorded at regular intervals.
• Datasets document metric evolution, such as intraday futures prices or weekly options open interest, that allows for pattern recognition, statisitcal modeling, and strategy automation.
• Common sampling frequencies for market data are tick-by-tick, minute, daily, or expiration cyclces.
• Structured time-indexed data forms the backbone of back-testing, allowing validation of hypotheses like momentum persistence or volatility mean reversion in electronic markets.

Trend

• Data patterns indicating movement towards relatively higher or lower values over an extended period are recognized by a rising or falling slope in the time serise.
• New popular songs may trend before slowly fading.
• Quantitative analysis of post-trend market behavior in derivatives indicates a low probability of sustained directional momentum re-establishment
• Factors like volatility clustering decay, liquidity redistribution, and participant positioning asymmetries typically constain trend formation.
• Empirical models often classify mean-reversion candidates particularly when post-trend volatility exceeds historical norms, and order book imbalances resolve.

Types of trends include:

• Uptrend: Ascending pattern in Time Series Analysis.
• Downtrend: Descending patter in Time Series Analysis.
• Horizontal or Stationary Trend: A consistent trend is not evident

Visualizations

• Data trends can be observed by plotting a dataset Steps:

1. Import the module
2. Load the Dataset
3. Convert the month column to a datetime object
4. Set the month as the index
5. Create the Plot

Decomposition

• Understand the complexities of visualization by utilizing the "seasonal_decompose" function.
• Function extracts and analyzes trend seasonality and residuals within time series data.
• Focus on the data's trend, which is accessed using "seasonal_decompose ().trend"
• The seasonal_decompose function employs the moving averages approach to determine the trend.

Python API

• This module builds competency in programmatic, financial data acquisition through Python APIs, with emphasis on tech work flows for systematic trading.
• Learners will engage REST API protocols to interface with market data providers/brokerage systems and will put in API keys to secure access
• Core competencies include: managing rate-limited requests; transforming JSON/XML payloads into time-series DataFrames; constructing fault-tolerant pipelines for continuous data ingestion.
• Focus will be on real-time derivatives pricing feeds, institutional-grade historical databases, and alternative datasets like volatility indices.
• The processes focus on turning raw API outputs into actionable inputs for strategy back-testing (volatility arbitrage signals) and liquidity analysis (order book depth streams).

API

• Application Programming Interface (API) sets up standardized protocols for software interoperability.
• APIs govern algorithmic trading platforms request/receive data from exchanges/market data vendors through communication frameworks.
• Interfaces define permissible operations (GET/POST requests), authentication schemas (HMAC signatures), data formatting conventions (JSON/XML), and data interchange between systems.
• A derivatives trading algorithm might transit a call to a futures' exchange order management system, specifying contract details/execution to receive real-time fill reports/position updates.

Making API Requests in Python

• Some tools, such as requests, are required so we install them. Command: pip install requests Command to import: import requests
• A programmatic request to Alpha Vantage's API was dispatched, specifying (equity symbol), ( intraday sampling interval), and ( API credentials) to get a response.
• HTTP GET method retrieves JSON payload containing timestamped OHLC data.
• Most recent key in the "Time Series" object is identified.
• "1. open" value extracted from the entry and converted into floating-point numeric monetary value with decimal precision. Output ex: IBM Latest 5-Minute Open: $185.34

API Status Codes

• Status codes accompany requests and provide outcome information indicating process of errors encountered.
• A "200" status code indicates success.
• Documentation states that the API response is in JSON format.

Dealing with JSON Data

• A Python script functions as a digital news curator by connecting to News API, locating business head lines form the U.S, then organizing and displaying the data.

What is IBAPI?

• The Python native API provided by Interactive Brokers enables automatic trading through Python code via a communication protocol between the Broker's servers and custom software applications.
• APIs function as programmatic intermediaries between market infrastucture trading systems.
• in derivatives trading, a Python system might dispatch limit orders for E-mini S &P 500 futures via a brokerage API while consuming real-time volatility surfaces from an options-data feed through HTTPS/webSocket protocols.

What Can You Achieve:

• Automate Trading: Whether fully- or semi-automated, the API automates script connectivity with Interactive Brokers.
• Build a Custom Trading Terminal: Though Interactive Brokers' TWS is feature-rich, API allows a customized one.
• Access Historical Data: Historical data is essential for automatic trading systems
• Develop Custom Indicators: While TWS includes standard indicators, API makes fully-customized indicators
• Python supports machine learning, sentimental analysis and further indicators.
• Set Up Custom Alerts and Notifications: API lets customer create alerts

Setting up the IB Native Python API

• Open an account with IB.
• Demo accounts for testing.
• The API in TWS offers a read-only option, helpful in initial familiarization.
• The IB Python native API provides programmatic access to retrieve real-time market data and manage derivatives portfolios.
• The API operates via a client-server architecture, where custom Python scripts interface with IB's Trader Workstation (TWS) or Gateway software.
• Download the Interactive Brokers client (TWS/IB Gateway).
• Interactive Brokers' default trading client is TWS; using it during early testing of scripts is advised because it offers visual guidance
• Switching to the Gateway is easy later on
• To download the Python Native API, navigate to Technology > Trading APIs > Get API Software, selecting API version 9.73 or later (prior version lack Python source files)
• Python 3.1 or later.

In the directory specified, deploy via Windows Installer then navigate to:

/TWS API/source/python client

• Command python3 setup.py install to install the API as a package. Run "import ibapi"in Python without errors to confirm a good installation with IB trading infrastructure.

Choose IDE

• IDEs coding, testing, debugging algorithmic trading systems, financial analysis/ API connectivity to brokerage platforms.
• Communication to coding servers is fundamental.
• Custom scripts interfact directly with HTTP(REST APIs) or WebSocket, which tailors execution logic however needs manual adherence protocols.
• Broker-specific IDEs with proprietary code regulate compliance with configure connectivity (Meta Quote Language) and its integrated IDE.

The IB APIs EClient (orders)reliably executes outgoing requests but EWrapper (prices)fails if socket breaks.

• Persistent connectivitiy is critical for data flows that are isynchronos.
• Third party library, IB-in sync, leverages the asyncio library to asynchronous single thread for API interaction.
• Popular IDEs: PyCharm, VS Code, Sublime Text, Atom recommended.
• VS Codes offers control w/ Git.
• Factors such as debugging efficiency, API connectivity are balanced based on preference.
• Specialized IDEs (Quant Connect) embed testing tools, deployment of live, mult-broker compatibility.

Test for Connectivity

• A basic code snippet shows how to test connection to the IB API with relevant modification of the app and connect if needed.
• The client ID and socket is crucial for idenitying your script
• Remember to modify the socket port number within the function app and connect if necessary.
• The client ID next to the socket port is crucial for identifying your script to the API and it should be unique integer.

In the IB Python API client folder, source code files like EClient and EWrapper are available.

• Eclient manages outgoing requests, while EWrapper deals w/ incoming messages as a wrapper

To access the documentation for EClient or EWrapper use:

• help (Ewrapper)"to access it- (access detailed Info for python or terminal access of the functions within these classes) Dual-class API structure merges outgoing/incorming channels, with "EClient" in charge of "Ewrapper".
• Connection protocols configures socket-basedlinks from Trader Workstation (TWS) or IB Gateway. Ports specify live and other testing environments
• Threaded processes maintain order tramission capabilties via markets

To obtain a stock's most recent ask price:

• Establish contracts that outline specifications as well as the use of of "regmktData", which displays the API request

Retrieving data will require:

• A customized function to display the response on the screen.
• API data management for low latency- (use for real time integration with volatile orders)
• In order to facilitate computational access, firms often combine the use of in-memory storage with databases.
• To get market data for EUR/USD; adjust remaining scripts via a TWS watchlist and select "Description" A -Pop up box- with needed Info will result

Getting the most recent ten hourly bars requires:

• fetching ask price