Momentum strategy python

For this post, I want to take a look at the concept of intra-day momentum and investigate whether we are able to identify any positive signs of such a phenomenon occurring across quite a large universe of NYSE stocks. Where their study lacked depth number of instruments studiedmy data contains around individual stocks, however, where they tested over a long time period 20 years my data spans only 1 year.

We can but try…. The chart below shows what we are looking for — a daily price path that displayed the same overall direction in the first 30 minutes as it does in the last 30 minutes at least 30 minutes is our starting gambit for a reasonable window as this is the window period used in the aforementioned research paper — we can perhaps play around with this value at some point.

The overall return for the two window periods can be either up or down, as long as daily moves are in the same direction. As in the previous post I shall be using data sourced from AlgoSeek. Even aside from the gulf in quality, it is just next to impossible to source intraday stock data for free at least in my experience.

Equities Market Intraday Momentum Strategy in Python – Part 1

I believe AlphaVantage still has an API that allows intra-day downloads, although I have used them before for various pet projects and research efforts and quickly realised my results were being badly affected by the dubious quality.

As always we begin with our module imports. I have also set the value of the default matplotlib figure to be 12 x 8, as I find the normal default value to be too small for my liking.

Saying as I was dealing with s of stocks over a 1-year periodeach one containing minute by minute data and an accompanying 57 odd columns of data per 1-minute bar i.

4020 salvage parts

Once extracted and moved across into a series of SQLite databases it grew 10x in size and currently sits at around GB. Dask, use of specialised Pandas arguments and methods to deal with limitations, paying special attention to data types used to store data etc. If we just run a few simple tests and time each one, we can get an idea of the speed up we can expect by substituting in feather files for the bog-standard CSV files we all usually default to.

I have a Pandas DataFrame that currently holds rows and columns, socells in total. It is showing as being 1. The first attempt registered at 6 minutes and 1 second for the complete write time. The clock registered Better than the 6 minutes taken to write the file to disk, but if you keep reading it in, again and again, that time is going to stack up!

Again…just 1. On another positive note Feather currently supports a relatively wide range of data types info can be found at the Github repo link pasted earlier. Iterate through the DataFrame columns, selecting 2 columns at a time and extracting only three rows from that data. The 3 rows we are interested in are those corresponding to the 10 am minute bar, the pm minute bar and the 4pm-close of trading minute bar. The rows in the DataFrame represent the close of that particular minute bar just as a reminder.A trading strategy is a fixed plan that is designed to achieve a profitable return by going long or short in markets.

A trading strategy should be backtested before it can be used in live markets. Strategies can be categorized as fundamental analysis, technical analysis, or algorithmic trading. In this article, we will focus on technical analysis. Technical analysis is a statistical methodology for forecasting the direction of prices through the study of past market data, primarily price, and volume.

Technical Analysis focuses on trend, support, resistance, and momentum through the use of chart reading to help investors and traders get into and out of higher probability trades. This article will focus on measuring the volatility and strength of stock prices.

Disclaimer: Do not trade with this strategy, using a trading strategy without backtesting is very risky and not recommended. The purpose of this article is to help you understand an easy way to calculate RSI and volatility values of stock prices. In simple terms, momentum is the speed of price changes in a stock. The basic idea of a momentum strategy is to buy and sell according to the strength of the recent stock prices.

momentum strategy python

The momentum is determined by factors such as trading volume and rate of price changes. Momentum traders bet that a stock price that is moving strongly in a given direction will continue to move in that direction until the trend loses strength. Why should momentum be part of a trading strategy? If you understand the fundamentals of trading, you know that trend is an important concept of technical analysis.

Trend indicates the general direction the market is moving in a specific period of time. A trend can be upward increase in price or downward decrease in price. Many strategies rely on identifying whether the market is in a trend or not — and from there, working out if a trend is beginning or coming to an end. Knowing whether a trend is starting up or just about to break down is an extremely useful piece of information to have at your disposal.

Part of knowing whether a trend will continue or not comes down to judging just how much strength lies behind the trend. This strength behind the trend is often referred to as momentum, and there are a number of indicators that attempt to measure it.

For this article, we will be using the RSI indicator. The RSI indicator provides signals that tell investors to buy when the security is oversold and to sell when it is overbought. High RSI usually above 70 may indicate a stock is overbought, therefore it is a sell signal. Low RSI usually below 30 indicates stock is oversold, which means a buy signal. According to wikipedia, Volatility is the degree of variation of a trading price series over time as measured by the standard deviation of logarithmic returns.

Volatility measures the risk of a security. It indicates the pricing behavior of the security and helps estimate the fluctuations that may happen in a short period of time. If the prices of a security fluctuate rapidly in a short time span, it is termed to have high volatility. If the prices of a security fluctuate slowly in a longer time span, it is termed to have low volatility.

Volatility can be easily calculated by finding the square root of the variance of a daily stock price. We will collect our historical data from Yahoo Finance using pandas.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am having a difficulty in averaging 1 to 12 efficiency ratio. Of course I know that it can be simply implemented by for loop and it's very easy task, but I failed.

Mage ethulata

You could simplify further by storing the values corresponding to p in a DF rather than computing for each series separately as shown:. Then, you could assign the repeating operations to a variable which reduces the re-computation time. Note: If speed is not a major concern, you could perform the operations via the built-in methods present in pandas instead of converting them into it's corresponding numpy array values.

How are we doing? Please help us improve Stack Overflow.

momentum strategy python

Take our short survey. Learn more. Momentum portfolio trend following quant simulation on pandas Ask Question. Asked 3 years, 5 months ago. Active 3 years, 5 months ago.

Equities Market Intraday Momentum Strategy in Python – Part 1

Viewed 3k times. I need more concise and refined code, anybody can help me? Thanks import pandas as pd import matplotlib. Wookeun Lee Wookeun Lee 3 3 silver badges 13 13 bronze badges. Active Oldest Votes. DataFrame fractal a, l. Nickil Maveli Nickil Maveli Thank you very much When I calculate elementwise operation between dataframes, sometimes error occurs unless I code with 'df.

What's the difference? Good question. As long as they are a part of the same dataframe, you could perform the arithmetic operations via broadcasting them.

Trend or Momentum based Trading Strategy

The problem arises when you want to multiply two dataframes element-wise or two series of them having a mismatch in the sizes which leads to your DF returning Nans. In those cases, you must convert it to it's numpy counterpart by accessing the.In another great post, Teddy Kokerhas shown again a path for the development of algotrading strategies:. Teddy Koker dropped me a message, asking if I could comment on the usage of backtrader. And my opinion can be seen below.

It is only my personal humble opinion, because as the author of backtrader I am biased as to how the platform could be best used.

And my personal taste about how to formulate certain constructs, does not have to match how other people prefer to use the platform. Actually, letting the platform open to plug almost anything and with different ways to do the same thing, was a conscious decision, to let people use it however they see fit within the constraints of what the platform aims to do, the language possibilities and the failed design decisions I made.

Here, we will just focus on things which could have been done in a different manner. Whether "different" is better or not is always a matter of opinion. And the author of backtrader does not always have to be right on what it is actually "better" for developing with "backtrader" because the actual development has to suit the developer and not the author of "backtrader".

For example from the code:. With a tuple of tuples parameters retain the order of declaration, which can be of importance when enumerating them. The declaration order should be no problem with default ordered dictionaries in Python 3. Use the forcei. To carry on, backtrader defines an OperationN indicator which must have an attribute func defined, which will get period bars passed as an argument and which will put the return value into the defined line. Which means that we have taken the complexity of the indicator outside of the indicator.

As a bonus we have purely declarative indicator. Use shorter and the shorter names for imports for exampleit will in most cases increase readability.

Don't use close for a data feed. Pass the data feed generically and it will use close. This may not seem relevant but it does help when trying to keep the code generic everywhere like in indicators. It does for sure sometimes fail, but it tries. As you may see, there is no need to keep the self. The length of the strategy and of most objects is provided, calculated and updated by the system all along the way.

A bar moving average will obviously only deliver when it has data points from the data feed. This means that when entering nextthe data feed will have data points to be examined and the moving average just 1 data point.

This is useful for example when several data feeds are in play and they start date is different. The developer may want some examination or action be taken, before all guarantees for all data feeds and associated indicators are met and next is called for the first time.

Because it would seem pointless to do this calculation for the entire life of a strategy, an optimization is possible such as this. The guard calculation is moved to prenext which will stopped being called when the guarantees are met. Using them will ensure that rebalancing happens when it is meant to happen. Let us imagine that the intention is to rebalance on Fridays.

And now we are ready to know when it is Friday. Use always a pre-built comparison rather than compare things during next.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time.

momentum strategy python

Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am having a difficulty in averaging 1 to 12 efficiency ratio. Of course I know that it can be simply implemented by for loop and it's very easy task, but I failed. You could simplify further by storing the values corresponding to p in a DF rather than computing for each series separately as shown:.

Then, you could assign the repeating operations to a variable which reduces the re-computation time. Note: If speed is not a major concern, you could perform the operations via the built-in methods present in pandas instead of converting them into it's corresponding numpy array values. Learn more. Momentum portfolio trend following quant simulation on pandas Ask Question.

Asked 3 years, 6 months ago. Active yesterday. Viewed 3k times. I need more concise and refined code, anybody can help me?

Mercedes benz gle wiring diagram diagram base website wiring

Wookeun Lee Wookeun Lee 3 3 silver badges 13 13 bronze badges. Active Oldest Votes. DataFrame fractal a, l. Nickil Maveli Nickil Maveli Thank you very much When I calculate elementwise operation between dataframes, sometimes error occurs unless I code with 'df.

momentum strategy python

What's the difference? Good question. As long as they are a part of the same dataframe, you could perform the arithmetic operations via broadcasting them. The problem arises when you want to multiply two dataframes element-wise or two series of them having a mismatch in the sizes which leads to your DF returning Nans. In those cases, you must convert it to it's numpy counterpart by accessing the.

Thank you for kind explanation, now I clearly got it, just the same I expected. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown.

The Overflow Blog. The Unfriendly Robot: Automatically flagging unwelcoming comments.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again.

If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. In this project, we will implement a momentum trading strategyand test it to see if it has the potential to be profitable. We are supplied with a universe of stocks and time range. We are also provided with a textual description of how to generate a trading signal based on a momentum indicator. We will then compute the signal for the time range given and apply it to the dataset to produce projected returns.

Finally, we will perform a statistical test on the mean of the returns to conclude if there is an alpha in the signal. For the dataset, we will use the end of day from Quotemedia.

We will also make things a little easier to run by narrowing down our range of time period instead of using all of the data.

Udacity doesn't have a license to redistribute the data to us. They are working on alternatives to this problem. If we try to graph all the stocks, it would be too much information. The trading signal we'll develop in this project does not need to be based on daily prices, for instance, we can use month-end prices to perform trading once a month. To do this, we must first resample the daily adjusted closing prices into monthly buckets, and select the last observation of each month.

A trading signal is a sequence of trading actions, or results that can be used to take trading actions. A common form is to produce a "long" and "short" portfolio of stocks on each date e. This signal can be interpreted as rebalancing your portfolio on each of those dates, entering long "buy" and short "sell" positions as indicated. For each month-end observation period, rank the stocks by previous returns, from the highest to the lowest. Select the top performing stocks for the long portfolio, and the bottom performing stocks for the short portfolio.

We'll start by computing the net returns this portfolio would return. For simplicity, we'll assume every stock gets an equal dollar amount of investment. This makes it easier to compute a portfolio's returns as the simple arithmetic average of the individual stock returns.

The annualized rate of return allows you to compare the rate of return from this strategy to other quoted rates of return, which are usually quoted on an annual basis.

Our null hypothesis H 0 is that the actual mean return from the signal is zero. We'll perform a one-sample, one-sided t-test on the observed mean return, to see if we can reject H 0. T-test returned a p-value of 0. This is a very high p-value so we cannot reject the null hypothesis.

We come to the conclusion from t-test that our signal was not strong enough to give us positive returns. In other words, our signal is not profitable. Modeling language for convex optimization problems. It allows you to express your problem in a natural way that follows the math, rather than in the restrictive standard form required by solvers.

The pyparsing module is an alternative approach to creating and executing simple grammars, vs. This library allows accurate and cross platform timezone calculations using Python 2. Six is a Python 2 and 3 compatibility library.A trading strategy is a fixed plan that is designed to achieve a profitable return by going long or short in markets.

A trading strategy should be backtested before it can be used in live markets. Strategies can be categorized as fundamental analysis, technical analysis, or algorithmic trading. In this article, we will focus on technical analysis. Technical analysis is a statistical methodology for forecasting the direction of prices through the study of past market data, primarily price, and volume. Technical Analysis focuses on trend, support, resistance, and momentum through the use of chart reading to help investors and traders get into and out of higher probability trades.

This article will focus on measuring the volatility and strength of stock prices. Disclaimer: Do not trade with this strategy, using a trading strategy without backtesting is very risky and not recommended.

The purpose of this article is to help you understand an easy way to calculate RSI and volatility values of stock prices. In simple terms, momentum is the speed of price changes in a stock. The basic idea of a momentum strategy is to buy and sell according to the strength of the recent stock prices.

The momentum is determined by factors such as trading volume and rate of price changes. Momentum traders bet that a stock price that is moving strongly in a given direction will continue to move in that direction until the trend loses strength. Why should momentum be part of a trading strategy? If you understand the fundamentals of trading, you know that trend is an important concept of technical analysis. Trend indicates the general direction the market is moving in a specific period of time.

A trend can be upward increase in price or downward decrease in price. Many strategies rely on identifying whether the market is in a trend or not — and from there, working out if a trend is beginning or coming to an end. Knowing whether a trend is starting up or just about to break down is an extremely useful piece of information to have at your disposal.

Part of knowing whether a trend will continue or not comes down to judging just how much strength lies behind the trend. This strength behind the trend is often referred to as momentum, and there are a number of indicators that attempt to measure it. For this article, we will be using the RSI indicator. The RSI indicator provides signals that tell investors to buy when the security is oversold and to sell when it is overbought.

High RSI usually above 70 may indicate a stock is overbought, therefore it is a sell signal. Low RSI usually below 30 indicates stock is oversold, which means a buy signal. According to wikipedia, Volatility is the degree of variation of a trading price series over time as measured by the standard deviation of logarithmic returns.

Volatility measures the risk of a security.

Rfid emulator

It indicates the pricing behavior of the security and helps estimate the fluctuations that may happen in a short period of time.


thoughts on “Momentum strategy python

Leave a Reply

Your email address will not be published. Required fields are marked *