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Jul 30 13 tweets 5 min read
9 trading strategies everyone should know (with Python code):
Bollinger Bands Pattern Recognition

The mid band is the moving average on the price series.

The upper and lower bands are two moving standard deviations away from the mid band.
MACD oscillator

MACD refers to Moving Average Convergence/Divergence.

MACD is a momentum trading strategy.

It assumes momentum has more impact on short-term moving average than long-term moving average.
Pairs Trading

Pairs trading is a basic form of statistical arbitrage.

It assumes that two cointegrated stocks do not drift too far away from each other.

When they do, it generates the trading signal.
Heikin-Ashi Candlestick

Heikin-Ashi refers to 'Average Bar' in Japanese.

It's an alternative style of candlestick chart.

The rules of Heiki-Ashi are designed to filter out the noise for momentum trading.
London Breakout

It’s an information arbitrage strategy across different markets in different time zones.

As FX markets are decentralized, you can take a peek at the activity in a closed foreign FX market before the opening of the domestic FX market.
Awesome Oscillator

Awesome oscillator is an upgraded version of the MACD oscillator.

Instead of taking a moving average on the close price, it uses the mean of high and low.

It uses short-term and long-term moving averages to construct the oscillator.
Dual Thrust

Dual thrust is an opening range breakout strategy.

We establish upper and lower thresholds based on previous day's open, close, high and low.

When the market opens and the price exceeds the thresholds, take positions before the thresholds.
Parabolic SAR

Parabolic SAR identifies the stop and reversal of a trend.

It's used to identify resistance to the price momentum.

Orders are executed when the SAR and price curves cross over.
Relative Strength Index Pattern Recognition

RSI reflects the current strength/weakness of the stock price momentum.

When RSI above 70 is overbought and RSI below 30 is oversold.
And if that isn't enough, there's 8 more strategies here:

github.com/je-suis-tm/qua…

And if THAT isn't enough, there's one more thing for you:
Check out the 46-Page Ultimate Guide to Pricing Options and Implied Volatility With Python.

Here's why:

• Compute Black-Scholes, the greeks and implied volatility
• Includes a Jupyter Notebook with the code
• How to use Python to analyze the results

pyquantnews.gumroad.com/l/46-page-ulti…
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More from @pyquantnews

Jul 29
Let's face it:

Creating beautiful charts for financial data in Python is not easy.

Axis formatting. Handling missing dates. Adding subplots.

We need a better way.

And now we have one:
mplfinance is a library that makes it simple to create beautiful charts of financial data:

• 22 example notebooks
• Plot user-defined data
• Includes built-in technical studies
• Animate data in Jupyter Notebook
• Plots OHLC bars, candlesticks, and line charts

Here's how:
Start by importing yfinance and mplfinance and downloading some data. Download data
Read 20 tweets
Jul 28
The most-used analytics software of the last 37 years:

Excel

But Excel on your resume is no longer enough to get a quant job.

Because Python is the new Excel.

But with 473,000,000 results for "python tutorial", most people struggle to start.

The 6 steps get started in 1 day:
In case you’re unsure if you should learn Python:

• 40% of all hedge fund jobs require it on job descriptions
• 100,000s of powerful pre-built packages
• Base salaries topping US$200,000
• Easy to learn for beginners
• Open source (FREE)

So… where do we start?
First, 3 dead-simple tips after 10 years of using Python:

• Study other people’s code (learn from the best)
• Find a real problem to solve (puzzles get boring)
• Learn how to ask questions (expect to ask A LOT of questions)

With that out of the way, let's go.

Ready?
Read 18 tweets
Jul 26
Data tables are great.

But nothing beats a pretty picture.

The only 12 Python visualization libraries you'll ever need: Weather data for Austin
Matplotlib: Visualization with Python

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible.

matplotlib.org
Plotly Dash

Dash is the original low-code framework for rapidly building data apps in Python. Dash is ideal for building and deploying data apps with customized user interfaces. It's particularly suited for anyone who works with data.

dash.plotly.com
Read 16 tweets
Jul 23
9 must-have Python time series libraries everyone working with data should know:
sktime

sktime is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks. Currently, this includes time series classification, regression, clustering, annotation and forecasting.

github.com/alan-turing-in…
tslearn

The machine learning toolkit for time series analysis in Python. Preprocess, train, and analyze machine learning models based on time series data.

github.com/tslearn-team/t…
Read 12 tweets
Jul 21
A Bloomberg Terminal costs $24,000 per year.

It's the portal to all the world's financial data.

Unaffordable for 99% of us.

Reserved for the Wall Street elite to outfox the normal investor.

Until now:
In case you're unfamiliar:

• Over 325,000 people use Bloomberg daily
• Used for market data, trading and research
• Launched in 1981 by billionaire Michael Bloomberg

It's a modern icon of financial markets.

So, how can you compete?
With OpenBB.

The OpenBB Terminal is a Python-based environment for investment research.

There are over 500 functions covering:

• ETFs
• Forex
• Stocks
• Crypto
• Options
• Portfolio
• Economy
• Mutual funds
• Econometrics

Strap on your seat belt.

Let's dive in:
Read 22 tweets
Jul 19
You should not underestimate the knowledge in books.

Videos and tutorials are great.

But don't forget the books (especially the classics).

The 7 must-have books everyone using Python for algorithmic trading needs to know:
Algorithmic Trading with Python: Quantitative Methods and Strategy Development

Algorithmic Trading with Python discusses modern quant trading methods in Python with a heavy focus on pandas, numpy, and scikit-learn.

amzn.to/3clB598
Algorithmic Trading with Interactive Brokers (Python and C++)

This book walks through the process of developing applications based on IB's Trader Workstation. Learn how to implement full-scale trading systems.

amzn.to/3ILI3QO
Read 11 tweets

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