Open Discord. Jared Vetto. Beginner here, using Python. What is the best way to do this? Also, is there some documentation of available methods I can search through? For example, I don't know how to find more about "self. Or search for relevant time methods. The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect.
In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal.
You should consult with an investment professional before making any investment decisions. Accepted Answer. Nevermind the second part - I have found that you can search the API tree in the Algorithm Lab environment under debug. Hi Jared Vetto, self. ClosePositions represents a scheduled event and it is defined in the algorithm. Scheduled events execute code at specific times of the day. We can define a scheduled event using the self.
On method. Here's an example:. Learn more about scheduled events in the documentation and in the boot camps, specifically check out the opening range breakout bootcamp. If you have any other questions, please don't hesitate to reach out! Best Rahul. You can continue your Boot Camp training progress from the terminal.
We hope to see you in the community soon! What is an Award? All Rights Reserved. Fama and French and Summers constructed a simple model for stock price that is the sum of a random walk and a stationary component - they represent the natural log of the stock price with x.
Balvers and Wu construct the log of stock prices as:. Using the equation above Serban adapts it to find the abnormal return in the forex market. Accounting for these changes:. We construct the portfolio by taking a long position on the currency with the highest expected return and taking a short position on the currency with the lowest expected return. We hold these positions for one month, and repeat the process each month.
There are two exceptions to this strategy: if all expected returns are positive, we take a long position only, and vice versa. If so the equation can be simplified as:. When applying the above equation, we found that the scale of the mean reversion for each currency are very different, and this difference in scale is large enough to affect the accuracy of our rank.
We made an adjustment to standardize the mean-reversion. This captures the mean reversion factor better than the author's technique. Each time we launch the strategy we use all of the available historical data prior to the start date to build the OLS model and uses that model for the entire backtest. We directly test our model on backtesting, because QuantConnect makes this easier.
In order to apply the model, we need to first pull history data to build it. The project can be briefly divided into four parts: the historical data request, model training, prediction and execution. The first function takes two arguments: symbol and number of daily data points requested. This function requests historical QuoteBars and builds it into a pandas DataFrame.
For more information about pandas DataFrame, please refer to the help documentation DataFrame. The concat function requests history and joins the results into a single DataFrame. We write it into a function because it's easier to change the formula here if we need. The predict function uses the history for the last 3 months, merges it into a DataFrame and then calculates the updated factors.
Using these updated factors together with the model we built we calculate the expected return. In the Initialize function we prepare the data and conduct a linear regression. The class property 'self. We will use this object each time we rebalance the portfolio. Every month we rebalance the portfolio using the Schedule Event helper method. The predicted returns are added to the rank array and then sorted by return.
The first element in the list is the best return paired with the associated symbol. When all the expected returns in the rank array are positive we only go long the pair with the highest expected return. When all returns are negative, we only go short the pair with the lowest expected return.
The following regression output is obtained by backtesting the time period from Jun to Jun From the regression statistics, the R-squared value is 1. We obtained 1. From these results we can say the limited sample size does not impair the feasibility of this model. The t-stats of the coefficients are The p-value of the reversal factor is very small which means this factor has a very high significance level. We performed some rough period sensitivity analysis in different time period from to and summarized the results as the following table:.
The paper demonstrates there are inefficiencies in the UIP which can be exploited with a hybrid momentum and mean reversion strategy. Although the sample size of the paper is much larger than ours the parameter and significance level of the two models are very close. To test this we wrote this implementation in the algorithm and commented out the lines.
If you are interested in exploring this extension to the model you can change these lines to test your strategy. You can also see our Documentation and Videos. You can also get in touch with us via Discord. Contents Abstract.
Generate real-time events - such as the end of day events. Trigger callbacks to real-time event handlers. For backtesting, this is mocked-up a works on simulated time. Configure the algorithm cash, portfolio and data requested. Initialize all state parameters required. This is because it is a great tool for working with your algorithms locally while still being able to deploy to the cloud and have access to Lean data.
It is also able to run algorithms on your local machine with your data through our official docker images. This section will cover how to install lean locally for you to use in your own environment. Refer to the following readme files for a detailed guide regarding using your local IDE with Lean:.
To install locally, download the zip file with the latest master and unzip it to your favorite location. Alternatively, install Git and clone the repo:. Visual Studio will automatically start to restore the Nuget packages. Alternatively, run the compiled dll file. Make sure you fix the ib-tws-dir and ib-controller-dir fields in the config. If after all you still receive connection refuse error, try changing the ib-port field in the config. A full explanation of the Python installation process can be found in the Algorithm.
Python project. Seamlessly develop locally in your favorite development environment, with full autocomplete and debugging support to quickly and easily identify problems with your strategy. For more information please see the CLI Home. Please submit bugs and feature requests as an issue to the Lean Repository. Before submitting an issue please read others to ensure it is not a duplicate.
Please use this to request assistance with your installations and setup questions. Contributions are warmly very welcomed but we ask you to read the existing code to see how it is formatted, commented and ensure contributions match the existing style. All code submissions must include accompanying tests.
Please see the contributor guide lines. All accepted pull requests will get a 2mo free Prime subscription on QuantConnect. Once your pull-request has been merged write to us at support quantconnect. The open-sourcing of QuantConnect would not have been possible without the support of the Pioneers. The Pioneers formed the core early adopters of QuantConnect who subscribed and allowed us to launch the project into open source. Skip to content. Star 6. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Branches Tags. Could not load branches. Could not load tags. Latest commit. Git stats 11, commits. Allows you to attend the course and get its certificate only to you. Allows you to attend the course and get its certificate to all the current and future members of the ' ' organization.
Sorry this member is not in your organization. To collaborate with other team members please ensure you have a Team subscription, and have added the collaborator to your organization. FormatNumber Math. Total After Credit. QuantConnect Learning Center. Recent Courses. What is Boot Camp? Your Quantitative Foundation A collection of courses from independent educators to improve your quant skill base and create better strategies. Solidify and expand your quant skill base with courses at QuantConnect.
Course Images: Course Header 3 x 1 Ratio. Previous Next. Lesson Outline. Task Objectives Completed Continue. Show Hint. Active Organization. Create New Algorithm Open Project. There are no recent projects in this organization. Delete Close Project. Algorithm Parameters. Getting Started. Learn The Platform. Exit Builder Mode.
Strategy Edit. Add Module. Content is hidden. Author: QuantConnect. Select Modules to add to you Algorithm. Author: Type:. Notice: Only the project owner can perform file updates at this time. Alpha code is read only. Lean local code is read only. Backtests Code Backtests. Optimization Strategy Parameters Constraints. Parameter Chart. Server Statistics. Downloading Backtest Results Close Create Chart. This backtest result is too large to be visualized in a web browser, please use this Link to download the raw result.
Select Chart. Alpha Ranking. Submit Alpha. Research Guide. Waiting for the backtest analysis to complete Reports can not be generated for backtest with runtime exceptions. Please write a project description to generate a backtest report. Request Report. Overall Statistics Download Results. Rolling Statistics.
Orders Summary Download Orders. Insights Summary Download Insights. Backtest Logs Download Logs. Make backtest public to share. Dedicated Report URL:. Dedicated backtest URL:. Embed this backtest into your website:. Analyzing Strategy. Launching Backtest. Waiting for Results. Deploy Live. Close Deploy. Select Environment Action.
Data Provider. Currency Amount Add Currency. Price Add Holding. Order Events. Close Email. Add Email Send email notification summaries of trades and portfolio state to interested parties. Phone Number. Add Webhook Trigger web requests to external servers on order and insight events. Add Telegram Steps to setup Telegram notifications: Create a new group where we'll send the notifications.
Get the Group ID: Using telegram web interface web. For example: web. Token optional. Automatically restart algorithm. Deploying Live Trading Strategy. Requesting New Live Trading Deployment. Logging into Brokerage. Initializing Algorithm. Successfully Deployed to NY7. Runtime Error:. Create Order. Create an Order. Cancel Submit Order.
Order Sent Successfully. Add Security. SecurityType Equity Forex Crypto. Market USA. Extended Market Hours. Live Logs Download Logs. Choose Optimization Strategy. Grid Search. Select Target. Target Value. Name Default Min Max. Parameter Type Decimal Integer.
Step Size. Back Next. Type and Number of Compute Nodes. Select Optimization Node Type. Maximum Allowed Nodes:. Estimated Number and Cost of Backtests. Estimated Total Backtest. Estimated Batch Time. Estimated Batch Cost. Estimated Number of Backtests. Back Launch Optimization. Backtests Optimizations. All Backtests Only Optimizations Only. Backtest Nodes. Node In Use By. Research Nodes. Live Trading Nodes. Automatically select best available.
All Projects. Project Name. Your Account. CPU-Hours Consumed. Remaining Daily Log. Backtest Log Limit. Account Settings. Code Automatic Builds. Code Completion. Parameter Highlighting. What is your preferred language? C Python. Light Theme Dark Theme. Manage Email Subscriptions. Transaction History. Send Request. Deactivate Account. Do you really want to cancel the subscription? Close Cancel Subscription. Please, first downgrade to free all your organizations.
Do you really want to delete your account? Close Deactivate Account. To continue please enter your email:. Cancel Continue. Search for Google Authenticator 3. Download and install the application Now open the Google Authenticator Application 4. Close Activate Two Factor. Live Projects Live Projects list the projects that have live results. Launch Algorithm. Looks like you don't have any live algorithms running.
Stop Liquidate. Deploy Project. Requesting coding environment No Coding Session Available Retry Please stop one of the following coding sessions, or upgrade your account. Loading coding environment
|Ipo discussion forums||Here's an example:. Quantity if self. I'm taking a look at the CustomTransactionModel example. I'm using the OCO implementation described here: Support for one-cancels-the-other order OCO by Levitikon I'm already using the highest resolution available and the width of the orders seems wide quantconnect forex market as suggested in the above thread? HI Interesting! The first element in the list is the best return paired with the associated symbol.|
|Quantconnect forex market||492|
|Mallard financial||Forex photos on the desktop|
|Marqeta shares exchange||505|
|Quantconnect forex market||728|
Backtest and live trade on your own signals, sourced from streaming, database, or file sources. Join a global community of quants, engineers, and scientists choosing LEAN for their algorithmic trading. Leverage the power of open-source for your fund.
Of code powering user strategies globally. Successfully deployed live on LEAN since Running live trading without interruption. LEAN is free to download and extend for commercial purposes. QuantConnect believes in the power of a community of passionate users. Check out our manifesto. We live this belief by making LEAN easy to use locally, and providing tutorials to ensure there is no vendor lock-in. LEAN Algorithm Framework bakes in key quantitative finance concepts, providing you a well-defined scaffolding as you start designing your algorithm.
The framework allows you to plug in modules created by the community and radically accelerate your process. Select a universe of assets with predefined filter criteria to reduce selection bias, or pick from one of the community universe selection models to quickly get an index of the most tradable assets. Fast-track algorithm development by spending the majority of your effort developing alpha insights, reusing pluggable modules for other parts of your algorithm. Using defined insights, create a weighted combination of assets to form your ideal portfolio.
Apply cutting-edge algorithms to execute your target portfolio efficiently and quickly. Pluggable framework-execution models allow you to quickly backtest different execution models and use your optimal execution strategy. Adjust position sizes and manage post-trade risk with plug-in risk models. Risk models can be passive or active by hedging exposed positions as required.
Combine multiple risk models to handle a range of market conditions. All assets are managed from a central portfolio, allowing you to trade on all 6 asset classes at the same time. Code locally in Visual Studio and backtest in the cloud with QuantConnect data and computing. Monitor your backtests from your Visual Studio control window. All Rights Reserved. Core Feature Set. Survivorship Bias Free Automated accounting for splits, dividends, and corporate events like delistings and mergers. Universe Selection Avoid selection bias with dynamically generated assets.
Portfolio Management Automatically track portfolio performance, profit and loss, and holdings across multiple asset classes and margin models in the same strategy. Scheduled Events Trigger regular functions to occur at desired times — during market hours, on certain days of the week, or at specific times of day. See a complete example or read the custom scripts docs.
Your IDE or editor is where you spend your time. That's why QuantRocket gives you choices. Real-time market data, powered by Alpaca or Interactive Brokers. Backtesting is only the first step. Once you go live, you need a clear picture of performance to assess whether live trading is mirroring your backtest.
The material on this website and any other materials created by QuantRocket LLC is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantRocket LLC. In addition, the material offers no opinion with respect to the suitability of any security or specific investment.
No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action. Neither QuantRocket LLC nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of , as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein.
If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to QuantRocket LLC about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. QuantRocket LLC makes no guarantees as to the accuracy or completeness of the views expressed in the website.
The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. Past performance is not indicative of future results. Interactive Brokers is not affiliated with and does not endorse or recommend QuantRocket LLC or any of its products or services. Email us at sales quantrocket.
Toggle navigation. Research and trade quantitative strategies in global markets using Python Start for free what's free? Global Data. Multiple Backtesters. Cloud or Local. Live Trading. Watch the Intro Video See how to run an intraday momentum strategy in QuantRocket, all the way from data collection to backtesting to live trading to performance tracking. Your browser cannnot play this video.
Global Data Made Easy. What will you ask the data? Which sectors have the worst average EPS? Which penny stocks have the highest borrow fees? Do stocks trend higher in the last half-hour when already up for the day? What is gold's return when crude oil futures are in contango?
Find your data in the Data Library. Escape crowded trades Academic research shows that market anomalies are more enduring in international markets. Find the right market Write your strategy code once and backtest it in numerous countries to find where it works best.
Validate your backtests A backtest that performs well across several global markets is more robust than one tested on a single market. Trade around the clock When the US closes, Asia opens. When Asia closes, Europe opens. Exploit profitable opportunities whenever they occur.
QuantRocket is tailor-made for global markets. Choose Your Backtester. One size does NOT fit all The backtester that's right for you depends on the style of your trading strategies. Moonshot Zipline Machine Learning Custom. Moonshot is the backtester for data scientists Key features: Based on Pandas, the centerpiece of the Python data science stack Fast, vectorized, multi-strategy backtests and parameter scans Daily or intraday strategies Equities, futures, and FX Live trading Open source, designed by and for QuantRocket.
The first professional-grade platform for live trading with Zipline Key features: Event-driven backtesting using Python 1-minute US stock data included Support for equities and futures Integrated support for related open-source libraries including Alphalens and Pyfolio Live trading with QuantRocket-built adapters. Learn more. First class support for machine learning strategies with MoonshotML Key features: Walk-forward optimization : Rolling and expanding walk-forward optimization, widely considered the best technique for validating machine learning models in finance.
Connect third-party backtesters or run custom scripts A hint of what's possible: Run third-party backtesters such as backtrader Schedule daily downloads of third party data Create an options trading script that uses QuantRocket's Python API to query data and place orders using the blotter Create and schedule multi-step maintenance tasks such as creating futures calendar spreads based on changing rollover rules. CrossOver smavg, lmavg.
Deploy With Ease. Run anywhere Linux, Mac, or Windows. In the cloud or on your laptop. QuantRocket runs anywhere Docker runs. Connect from anywhere Control your cloud-based deployment securely from any location using QuantRocket's JupyterLab web interface.
Flexible architecture Use QuantRocket as a standalone end-to-end trading platform, or connect to it from other trading applications to query data, submit orders, or use other components you need. Your servers, your way Hosted platforms like QuantConnect limit your compute resources and require uploading your secrets to third party servers. QuantRocket's ready-to-use trading infrastructure runs on your servers.
Give your hardware as much power as you want, and keep your secrets safe. Multiple editors Your IDE or editor is where you spend your time. Complement JupyterLab with intelligent code editing in the browser with Eclipse Theia.