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Jul 30, 2022 13 tweets 5 min read Read on X
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 16
One way to get started with algorithmic trading:

Buying a $120,000 master's degree from MIT.

The other way:

YouTube.

The 10 YouTube videos to get you up and running today:
Algorithmic Trading Using Python - Full Course

A comprehensive course on algorithmic trading using Python.

Full Algorithmic Trading Python Course

An extensive playlist covering Python for algorithmic trading.

youtube.com/playlist?list=…
Read 13 tweets
Jun 8
Steal this 8-step framework to take an algorithmic trading strategy from idea to execution.
1/ The idea

Look for market anomalies that repeat.

Your strategy should be based on some economic rationale. In other words, “using machine learning” is not a trading strategy.
2/ Research

Find out if the anomaly you think you found actually exists in history.

Get data and start exploring.
Read 11 tweets
May 30
An 8-step, dead-simple algorithmic trading framework billion hedge fund managers follow: Image
1/ The idea

Look for market anomalies that repeat.

Your strategy should be based on some economic rationale. In other words, “using machine learning” is not a trading strategy.
2/ Research

Find out if the anomaly you think you found actually exists in history.

Get data and start exploring.
Read 10 tweets
May 22
There is $664,000,000,000 invested in factor strategies.

Factors can help you manage risk and amplify returns.

I spent 3 years figuring it out.

Now you can do it in 10 minutes.

Here’s how in Python.
By reading this thread, you’ll be able to:

• Download historic factor data
• Compute the sensitivities to the factors
• Figure out the risk contribution of the factors

But first…
A quick primer on factor investing:

• Used to target specific return drivers
• Helps manage risk outside diversification
• Important for active managers that get paid for performance

You can use the famous Fama-French 3-factor model for free.

Here’s how.
Read 13 tweets
May 8
I read the 161-page Financial Machine Learning paper.

It took me 3 days.

Here are the key topics (in 30 seconds): Image
Understanding Asset Prices

Prices as predictions, reflecting future payoffs and investor valuations.
Large Information Sets

The vast scope of information influencing market prices, highlights the need for complex models.
Read 11 tweets
May 4
There is $664,000,000,000 invested in factor strategies.

Factors can help you manage risk and amplify returns.

I spent 3 years figuring it out.

Now you can do it in 10 minutes.

Here’s how in Python.
By reading this thread, you’ll be able to:

• Download historic factor data
• Compute the sensitivities to the factors
• Figure out the risk contribution of the factors

But first…
A quick primer on factor investing:

• Used to target specific return drivers
• Helps manage risk outside diversification
• Important for active managers that get paid for performance

You can use the famous Fama-French 3-factor model for free.

Here’s how.
Read 13 tweets

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