Python Library To Run Quantopian Algorithm In Live

Quantopian — The Online Algo Trading Platform

Quantopian is one of the most popular online algo trading platforms and communities today. It provides the great backtesting environment where you can experiment with your idea, build algorithms and even participate in the contest, as well as share the idea and discuss it with smart people there.

Photo by  Rodion Kutsaev  on  Unsplash

One of the things many people have asked Alpaca during the beta program is how to run the algorithms that they built in Quantopian platform for their own purpose, not just for the contest. While Quantopian has built so much in the platform, they are so great to share the internal framework as open source zipline.

The Newest Open Source Libraries for Quantopian Users

Today, I wanted to share our newest open source libraries for Quantopian users; pylivetrader and pipeline-live.

alpacahq/pylivetrader
Python live trade execution library with zipline interface. - alpacahq/pylivetradergithub.com

alpacahq/pipeline-live
Pipeline Extension for Live Trading. Contribute to alpacahq/pipeline-live development by creating an account on GitHub.github.com

pylivetrader is a zipline API compatible trading framework in python which again focuses on live trading, with much less overhead and dependency problems. It is written from the ground up for live trading use cases, so it removes a lot of heavy lifting that zipline had to do such as price adjustment etc.

This means, you don’t need to build your data bundle to kick off your algorithm in live, but instead you can just start your live trading from the Quantopian algorithm source right away.

At the moment, the supported backend is only Alpaca, but we are happy to connect to IB etc. if someone contributes the code.

Pipeline API — the Core Piece of Quantopian Framework

Pipeline API is the core piece of Quantopian algorithm framework that allows easy stock selection based on the different metrics, much in a pythonic way, and this differentiates the platform from others. I found Pipeline is providing a tremendous value when it comes to trading wide range of universe. Unfortunately, it is not so easy for most people to use this great feature outside of the Quantopian platform.

pipeline-live is a python tool that allows you to do something similar anywhere so that you can do your research somewhere else as well as use it with existing python trading framework such as zipline-live or backtrader, including pylivetrader which I am introducing below. pipeline-live primarily uses IEX public API for pricing and basic fundamental information.

As you know, IEX provides market-wide volume data for daily OHLCV which makes it a perfect choice for pipeline usage. Since pipeline-live focuses on live trading use cases, it does not provide historical view unlike inside Quantopian, but the upside is it is fairly independent and easy to use. It is also very extensible so you can hook up with other paid data sources if you would find useful.

How to Convert Your Quantopian Algorithms to Run in Live Trading

We also put some practices together about how you could convert your Quantopian algorithms to run in live trading. You may want to take a look at these documents if you are interested in.

https://github.com/alpacahq/pipeline-live/blob/master/migration.md
https://github.com/alpacahq/pylivetrader/blob/master/migration.md

I also posted in Quantopian forum with the real example, and you may take a look at it, too.

Long-only non-day trading algorithm for live
This is a modified version of the algorithm presented in…www.quantopian.com

Feel free to give me any feedback/questions/criticism. Happy to help you get started with live trading with these tools too.

And here is the example code migrated from the post above.


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9 Great Tools for Algo Trading

In the last 5–10 years algorithmic trading, or algo trading, has gained popularity with the individual investor. The rise in popularity has been accompanied by a proliferation of tools and services, to both test and trade with algorithms. I’ve put together a list of 9 tools you should consider using for your algo trading process.

Photo by  Adrian Curiel  on  Unsplash

Photo by Adrian Curiel on Unsplash

Web Services:

The following are managed-services that you can use through web browsers, and don’t require much setup from the user. As someone who’s recently started in this field, I found it easy for new algo traders to try out.

(1) Quantopian:

1_xmqbJh3dGQIXylJTSCD7dA.png

A Boston-based crowd-sourced hedge fund, Quantopian provides an online IDE to backtest algorithms. Their platform is built with python, and all algorithms are implemented in Python. When testing algorithms, users have the option of a quick backtest, or a larger full backtest, and are provided the visual of portfolio performance.

Live-trading was discontinued in September 2017, but still provide a large range of historical data. They also have a serious community of developers, and now hold an ongoing DAILY contest with 10 winners awarded each day for a total of $5,000 per month in prize money (*updated from previously written as "periodically hold contests"). Quantopian provides capital to the winning algorithm. 

(2) QuantConnect:

1_eQopy9i2kr63RFeCFQq-sA.png

QuantConnect, is another platform that provides an IDE to both backtest and live-trade algorithmically. Their platform was built using C#, and users have the options to test algorithms in multiple languages, including both C# and Python.

QuantConnect also embraces a great community from all over the world, and provides access to equities, futures, forex and crypto trading. They offer live-trading integration with various names such as InteractiveBrokers, OANDA, and GDAX.

(3) QuantRocket:

QuantRocket is a platform that offers both backtesting and live trading with InteractiveBrokers, with live trading capabilities on forex as well as US equities. It’s specifically designed for trading with InteractiveBrokers, and sets itself apart with its flexibility.

QuantRocket supports multiple engines — its own Moonshot, as well as third party engines chosen by the user. While QuantRocket doesn’t have a traditional IDE, it is integrated well with Jupyter to produce something similar. One thing to keep in mind is that QuantRocket is not free. Pricing plans start at 19.99/month USD, with annual options.

Local Backtesting/LiveTrading Engines:

In today’s software world, you have lots more freedom if you make some effort outside of those managed-services. If you are comfortable this way, I recommend backtesting locally with these tools:

(4) Zipline/Zipline-Live:

quantopian/zipline
zipline - Zipline, a Pythonic Algorithmic Trading Librarygithub.com

Quantopian’s IDE is built on the back of Zipline, an open source backtesting engine for trading algorithms. Zipline runs locally, and can be configured to run in virtual environments and Docker containers as well. Zipline comes with all of Quantopian’s functions, but not all of its data. To balance that, users can write custom data to backtest on. Zipline also provides raw data from backtests, allowing for versatile uses of visualization.

Zipline discontinued live trading in 2017, but there is an open source project Zipline-live that works with Interactive Brokers. It has many of the same features Zipline does, and provides live trading.

(5) BackTrader:

backtrader - Backtesting / Trading
from datetime import datetime import backtrader as bt class SmaCross(bt.SignalStrategy): params = (('pfast', 10)…www.backtrader.com

Backtrader is currently one of the most popular backtesting engines available. It was built using python, and has a clean, simple, and efficient interface that runs locally (no Web Interface). One thing to keep in mind, backtrader doesn’t come with any data, but you can hook up your own market data in csv and other formats pretty easily.

Starting with release 1.5.0, BackTrader has live-trading capabilities. It’s been a popular choice with algo traders, especially after Zipline discontinued live trading.

(6) IBPy:

blampe/IbPy
IbPy - Python API for the Interactive Brokers on-line trading system.github.com

IBPy is an unaffiliated third party python wrapper for InteractiveBroker’s Trade Workstation API. Before IB started providing their official API library for python, this was the only way to connect to TWS for algorithms written in python.

IB has released an official python SDK, and this library is heading towards begin obsolete(while still being relevant for python2 users). But there still remain a significant number of live trading engines/tools that still use this library, and it’s good learning material for whoever wants to learn about implementing API’s.

While it’s good to learn about this library since it’s ubiquitous, if you are starting fresh, we recommend IB’s official python SDK.

Alpaca Trade API Python SDK is even much simpler to use!

Analytical Tools:

Back testing will output a significant amount of raw data. Some IDE’s will provide basic visualization and analysis, usually algorithm performance. If you’re looking for deeper evaluation, I recommend these tools:

(7) Pyfolio:

quantopian/pyfolio
pyfolio - Portfolio and risk analytics in Pythongithub.com

Pyfolio is another open source tool developed by Quantopian that focuses on evaluating a portfolio. What sets Pyfolio apart, is its ability to introduce degrees of uncertainty to a static set of data points, and evaluate Bayesian metrics from the user’s portfolio. The Pyfolio API offers a number of visualizations, which can be found on their GitHub repository.

(8) Alphalens:

quantopian/alphalens
alphalens - Performance analysis of predictive (alpha) stock factorsgithub.com

Alphalens is also an analysis tool from Quantopian. Unlike Pyfolio, Alphalens works well with the raw data output from Zipline, and rather than evaluate the portfolio, is performance analysis of predictive stock factors. Alphalens has its own range of visualizations found on their GitHub repository.

1_HnTL1cG_aBDoyBDyOzfkew.png

Median Daily Returns by Factor Quantile — one of the visualizations that alphalens offers

(9)TradingView:

1_Vy5CbHA0AdW7pwSmiwoZng.png

TradingView is a visualization tool with a vibrant open-source community. It’s entirely web-based, and allows users to visualize data, whether the data is the result of paper trading or algorithmic back-testing. Like Quantopian, TradingView allows users to share their results and visualizations with others in the community, and receive feedback.

(Bonus) Execution Platforms aka Broker-Dealers:

(10)InteractiveBrokers:

1_jAUoYLH-Fa3CyOtsfzsW3w.png

InteractiveBrokers is an online broker-dealer for active traders in general. They have been in the market since 1978. Algo trading isn’t IB’s focus, but multiple engines offer live trading through integration with their Trader Workstation. We’ve mentioned IB several times in this article — they’re just that good!

(11)Alpaca:

1_bhSUXCHVNP_JQmsMHklPlg.png

Finally, Alpaca! Alpaca was founded in 2015, and is an up and coming commission-free, broker-dealer designed specifically for algo trading. Alpaca also has a trade api, along with multiple open-source tools, which include a database optimized for time-series financial data known as the MarketStore.

The brokerage is scheduled to be publicly available this September (you can play around with the MarketStore right now), but if you can’t wait, head over to our website and jump on the waitlist for a chance at early access!

Alpaca | Algo Trading Commission Free with REST API
Lower your cost, Maximize your profits. Algo trading commision free.alpaca.markets

Miscellaneous Tools to Take a Look At:

  • qtpylib — another simplistic python backtesting engine
  • Multicharts — proprietary trading platform for forex and equities
  • WealthLab — desktop tool which allows C# backtesting, with live trading exclusive to Fidelity
  • Enygma Catalyst — for crypto trading
  • MetaTrader — backtesting/livetrading desktop app, de-fact in forex

I hope this quick primer on tools available right now was useful. If you liked it, please leave a clap (or two, I don’t mind). If you think there are tools that I missed, leave a comment below! I always appreciate any, and all feedback.

by Rao Vinnakota

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I am a College Student and I Built My Own Robo Advisor

I’m Rao, and I’m an intern at Alpaca working on building an open-source robo advisor. I don’t have much experience in the space, and I had to find a lot of answers. While there’s a wealth of material available on the web, very little is organized. This post is the combination of various threads and forums that I read through looking for answers.

“Yes, you an do it too!”

“Yes, you an do it too!”

What is a Robo Advisor anyway?!

Robo advisors are automated advising services that require little to no user interaction. They specialize in maintaining portfolios based on the investors chosen risk level. Btw, they were launched at the start of the financial crisis in 2008.

The actual logic for a robo advisor is straightforward.

  • Allocation” — Given a risk level, portions of capital are allocated to different positions.
  • Distance” — Over a regular time interval, the adviser scans to see if there’s a significant change in the portfolio balance, and
  • Rebalancing” — If necessary, rebalances.

Allocation:

The process of allocation is where different weights are assigned to asset classes based on the developer’s algorithm. These asset classes cover the various aspects of the market, and keep a diversified portfolio. Wealthfront sees these asset classes as:

  • US Stocks
  • Foreign Stocks
  • US Bonds
  • Foreign Bonds
  • Inflation Protection

Each of these assets is represented with ETFs, who capture a broad look at the asset class.

Distance:

Rebalancing a portfolio comes with a certain amount of overhead and cost. To avoid this, the robo advisor computes a “distance” vector. To trigger a rebalance, this distance will have to be greater than a given threshold. Most robo advisors usually have this threshold set to 5 percent.

The distance in this case, is the individual change in weight of each position. This is done by first calculating the current allocation. This is the value of a position against the total value of the portfolio. If any one position has a weight that’s 5 percent greater (current_weight/target_weight > 1.05), then the robo advisor will trigger a rebalance

Rebalancing:

Rebalancing is the act of buying and selling to return the portfolio to its target state. It’s important that all selling is done first. Once all cash available is gathered, necessary purchases are made.

To calculate how much to sell, use the target allocation of cash (weight * portfolio value), and see the number of shares that result from that cash. The difference between the number of shares currently held and the target level of shares is the amount to buy/sell.

Implementing a Robo Advisor with the Vanguard Core Series:

Now that we’ve established the various parts of what makes a robo advisor, we’ll start executing the steps to build one. We’ll build our robo advisor using Quantopian’s IDE.

When implementing our advisor, I find it easiest to do in steps. Quantopian’s IDE can only run backtests on a full algorithm. What that means, is there’s no “halfway” development. So, each step is a self-contained algorithm by itself.

Note: The implementation of Vanguard’s Core Series is using the information found here. The portfolio includes a 2% investment in a money market fund, but that isn’t available on Quantopian, so for the purposes of this tutorial, we’ll ignore it.

Risk-Based Allocation:

The first step, is to determine our universe, and assign weights using risk level. Our universe will be the Vanguard Core series:

  • VTI — Domestic Equity
  • VXUS — International Equity
  • BND — Domestic Fixed Income (Bonds)
  • BNDX — International Fixed Income (Bonds)

The risk level is actually the ratio of fixed income to equity, with equity being more volatile. So, a risk level 0 would have all capital allocated to fixed income (BND, BNDX), while a risk level of 5 would be 50/50. All 11 possible risk levels are catalogued in a dictionary, with the keys being the risk level, and values being tuples containing weight allocation:

risk_level = 5
risk_based_allocation = {0: (0,0,0.686,0.294),
                         1: (0.059,0.039,0.617,0.265),
                         2: (0.118,0.078,0.549,0.235),
                         3: (0.176,0.118,0.480,0.206),
                         4: (0.235,0.157,0.412,0.176),
                         5: (0.294,0.196,0.343,0.147), 
                         6: (0.353,0.235,0.274,0.118),
                         7: (0.412,0.274,0.206,0.088),
                         8: (0.470,0.314,0.137,0.059),
                         9: (0.529,0.353,0.069,0.029),
                         10: (0.588,0.392,0,0)}

The next step will be to implement our allocation in Quantopian. For this step, the only methods we’ll need are initialize and handle_data. The rest are superfluous.

The initialize method is the main. When a backtest is started, it automatically calls initialize. So, initialize will be the function that contains the dictionary for risk levels, as well as choosing the risk levels. The handle_data method is called automatically after initialize. It’s here that we’ll actually purchase the positions outlined in initialize.

Both initialize and handle_data have the variables context and data. Context allows the user to store global variables that get passed from method to method. Data helps the algorithm fetch different sorts of data. (Note: This is a change from Quantopian 1 where data was the object where global variables were stored).

Let’s get started. In initialize, copy and paste the following code:

context.stocks = symbols(‘VTI’, ‘VXUS’, ‘BND’, ‘BNDX’)
context.bought = False

risk_level = 5
risk_based_allocation = {0: (0,0,0.686,0.294),
                         1: (0.059,0.039,0.617,0.265),
                         2: (0.118,0.078,0.549,0.235),
                         3: (0.176,0.118,0.480,0.206),
                         4: (0.235,0.157,0.412,0.176),
                         5: (0.294,0.196,0.343,0.147),
                         6: (0.353,0.235,0.274,0.118),
                         7: (0.412,0.274,0.206,0.088),
                         8: (0.470,0.314,0.137,0.059),
                         9: (0.529,0.353,0.069,0.029),
                         10: (0.588,0.392,0,0)}
    #Saves the weights to easily access during rebalance
context.target_allocation = dict(zip(context.stocks,
                            risk_based_allocation[risk_level]))

    #To make initial purchase
context.bought = False

The variable context.stocks is a list of stock objects. The symbols function turns the strings into objects. The objects have attributes like current price, closing price, etc. Using that list, as well as the dictionary for allocation, we’ll create a second dictionary context.target_allocation. The keys are each ticker (VTI, VXUS, etc.) and the values are the ticker’s weights. This dictionary will be useful for both allocation and rebalancing.

Copy and paste the following code to create handle_data:

if not context.bought:
        for stock in context.stocks:
            amount = (context.target_allocation[stock] *
                      context.portfolio.cash) / data.current(stock,’price’)
            if (amount != 0):
                order(stock, int(amount))
                #log purchase
            log.info(“buying “ + str(int(amount)) + “ shares of “ +
                     str(stock))

         #now won’t purchase again and again
         context.bought = True

The variable context.bought refers to the value originally set to False in initialize. Since handle_data is called every time a market event occurs. So, using context.bought ensures the shares are only bought once.

To buy shares for each stock, the list of stock objects is iterated through. For each stock, the total number of shares are calculated by allocating the proper amount of capital (weight * capital), and then dividing that capital by the current price. Since we can only buy whole shares, leftover capital is added back to the cash on hand.

Lastly, for a smoother backtesting experience, all transactions are logged. The log is in the lower right hand corner. Build the current algorithm. Set the start date to June 2013 or later as the ETF BNDX was first tradeable on June 7, 2013.

Calculating Distance:

Before rebalancing a portfolio, it’s best to first calculate if the current portfolio is worth rebalancing. In more sophisticated robo advisors, this step would have a target portfolio calculated with an algorithm. For this example, our target portfolio is going to be the initial allocation defined by the risk level in initialize.

As the value of each position fluctuates, their portfolio weight may change too. The distance vector will be the change in weight of each individual position. Calculating the distance takes place in three steps:

  1. Calculating the current weights of each position
  2. Calculating the target weights (already done)
  3. Comparing the two weights

We want to check the balance daily, so we’ll go ahead and add the following function , before_trading_starts, to our code:

def before_trading_starts(context, data):
    #total value
    value = context.portfolio.portfolio_value +
                                         context.portfolio.cash
    #calculating current weights for each position
    for stock in context.stocks:
        if (context.target_allocation[stock] == 0):
            continue
        current_holdings = data.current(stock,’close’) *
                      context.portfolio.positions[stock].amount
        weight = current_holdings/value
        growth = float(weight) /
                        float(context.target_allocation[stock])
        if (growth >= 1.025 or growth <= 0.975):
            log.info(“Need to rebalance portfolio”)
            break

We first calculate total value. Since that value doesn’t change, it can be down outside the loop. Then, each position’s individual weight is calculated, and compared to the target weight (we’re using division since we’re interested in the relative growth of the position, not the absolute). If that growth exceeds the threshold (currently 2.5%) or is under the threshold (de-valued), then a rebalance is triggered. We don’t have an actual rebalance function written, so for now, we’ll simply log that a rebalance is necessary. It’s important to note the break added. Once the algorithm realizes that it’s time to rebalance, there’s no need to continue checking the rest of the stock. It creates a better best-case scenario.

However, we’re not actually done. We have to call before_trading_start, but calling it in a loop is inefficient. That’s why we’ll use the schedule_function command in initialize. Add this line of code to the end of the function block:

    schedule_function(
    func=before_trading_starts,
    date_rule=date_rules.every_day(),
    time_rule=time_rules.market_open(hours=1))

This schedules a distance calculation as the market opens every day. By computing the distance in the morning, we have the time and space necessary to execute rebalancing actions.

Rebalancing

The act of rebalancing a portfolio occurs in two steps. First, all assets that need to be sold are sold, and then all assets that need to bought are bought. Selling first makes sure that we don’t run out of cash.

The following is the code for a rebalance function:

def rebalance(context, data):
    for stock in context.stocks:
        current_weight = (data.current(stock, ‘close’) *
                          context.portfolio.positions[stock].amount) /
                          context.portfolio.portfolio_value
        target_weight = context.target_allocation[stock]
        distance = current_weight — target_weight
        if (distance > 0):
            amount = -1 * (distance * context.portfolio.portfolio_value) /
                     data.current(stock,’close’)
            if (int(amount) == 0):
                continue
            log.info(“Selling “ + str(int(amount)) + “ shares of “ +
                      str(stock))
            order(stock, int(amount))
    for stock in context.stocks:
        current_weight = (data.current(stock, ‘close’) *
                          context.portfolio.positions[stock].amount) /
                          context.portfolio.portfolio_value
        target_weight = context.target_allocation[stock]
        distance = current_weight — target_weight
        if (distance < 0):
            amount = -1 * (distance * context.portfolio.portfolio_value) /
                     data.current(stock,’close’)
            if (int(amount) == 0):
                continue
            log.info(“Buying “ + str(int(amount)) + “ shares of “ +
                      str(stock))
            order(stock, int(amount))

The selling and buying mechanics are the same. The only difference is when selling stock, you use a negative value when calling the order function. In both cases, we take the absolute difference in weight (target — current), and use that value to find the number of shares to buy or sell.

Multiple Universes

Vanguard provides several more universes beyond the core-series. Currently, we’re able to manipulate the risk level, and observe the outcome. Let’s add the functionality of also being able to select a universe.

The first and straightforward method, is to implement multiple dictionaries. Here’s how something like that would look. The CRSP series was added to our algorithm. The user now chooses both the universe and the risk level.

    core_series = symbols(‘VTI’, ‘VXUS’, ‘BND’, ‘BNDX’)
    crsp_series = symbols(‘VUG’, ‘VTV’, ‘VB’, ‘VEA’, ‘VWO’, ‘BSV’, ‘BIV’, ‘BLV’, ‘VMBS’, ‘BNDX’)
    #universe risk based allocation
    core_series_weights = {0: (0,0,0.686,0.294),
                           1: (0.059,0.039,0.617,0.265),
                           2: (0.118,0.078,0.549,0.235),
                           3: (0.176,0.118,0.480,0.206),
                           4: (0.235,0.157,0.412,0.176),
                           5: (0.294,0.196,0.343,0.147),
                           6: (0.353,0.235,0.274,0.118),
                           7: (0.412,0.274,0.206,0.088),
                           8: (0.470,0.314,0.137,0.059),
                           9: (0.529,0.353,0.069,0.029),
                           10: (0.588,0.392,0,0)}
    crsp_series_weights = {0: (0,0,0,0,0,0.273,0.14,0.123,0.15,0.294),
1: (0.024,0.027,0.008,0.03,0.009,0.245,0.126,0.111,0.135,0.265),
2: (0.048,0.054,0.016,0.061,0.017,0.218,0.112,0.099,0.12,0.235),
3: (0.072,0.082,0.022,0.091,0.027,0.191,0.098,0.086,0.105,0.206),
4: (0.096,0.109,0.03,0.122,0.035,0.164,0.084,0.074,0.09,0.176),
5: (0.120,0.136,0.038,0.152,0.044,0.126,0.07,0.062,0.075,0.147),
6: (0.143,0.163,0.047,0.182,0.053,0.109,0.056,0.049,0.06,0.118),
7: (0.167,0.190,0.055,0.213,0.061,0.082,0.042,0.037,0.045,0.088),
8: (0.191,0.217,0.062,0.243,0.071,0.055,0.028,0.024,0.030,0.059),
9: (0.215,0.245,0.069,0.274,0.079,0.027,0.014,0.013,0.015,0.029),
10: (0.239,0.272,0.077,0.304,0.088,0,0,0,0,0)}

    #set universe and risk level
    context.stocks = crsp_series
    risk_based_allocation = crsp_series_weights
    risk_level = 1

The user can use the three variables, context.stocks, risk_based_allocation, and risk_level, to set both the universe and the risk level.

What’s Next

Developing in Quantopian is a great experience — they provide lots of useful tools. But, it’s also limiting, being forced to only work in one file, etc. To build further on this robo advisor, I plan to migrate my work so far to the backend, and use Quantopian’s open source python library Zipline to run backtests. The next installment will cover all this and more!

You can find the algorithm here. Try it yourself!

by Rao Vinnakota

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Algo Trading News Headlines 6/1/2018

Getting Started in Algorithmic Trading Includes Overcoming Common Problems

(www.finextra.com)

A trading algorithm takes time to develop. Most people get stuck quickly and even give up. When you get stuck, or your algo doesn’t show progress, you need to stop and re-evaluate. An approach that works well within most algo shops is to break the strategy down into small components.

Photo by  Yung Chang &nbsp;on  Unsplash

Photo by Yung Chang on Unsplash

Emporio Trading Explains the Benefits of Algorithmic Trading

(www.digitaljournal.com)

According to a recent study from FinTech research company Technavio, algorithm trading is expected to continue growing at a global annual rate of 10.3 percent through the year 2020. Emporio Trading explains why traditional investment institutions are building tech-savvy algorithm platforms to help improve market intelligence and long-term trade decisions.

Algorithmic Trading: Using Quantopian’s Zipline Python Library In R And Backtest Optimizations By Grid Search And Parallel Processing

(www.r-bloggers.com)

We are ready to demo our new experimental package for Algorithmic Trading, flyingfox, which uses reticulate to bring Quantopian’s open source algorithmic trading Python library, Zipline, to R. This article includes a long-form code tutorial on how to perform backtest optimizations of trading algorithms via grid search and parallel processing.

Global Automated Trading Market: Ability to Offer End to End Solutions to Drive Market Growth

(www.satprnews.com)

The global automated trading market is expected to witness high growth during the forecast period. The report provides key statistics on the market status of the leading market players and offers key trends and opportunities in the market. Automated trading system helps to let traders develop specific rules for trade entries and exits that were once programmed and now can be automatically executed via a computer. This program automatically helps to generate orders which are constructed on predefined set of rules which is based on technical analysis or input from other electronic sources.

NSE Algo Scam: CBI Steps in Where SEBI Was Pussyfooting

(www.moneylife.in)

In a sensational twist to the investigation into the misuse of co-location facilities of the National Stock Exchange (NSE) by select brokers (also known as the algo scam), the Central Bureau of Investigation (CBI) seems to have sidelined the powerful market regulator and has filed a first information report (FIR) on 30 May 2018. The FIR lists unnamed officials of the NSE, the Securities and Exchange Board of India (SEBI) and specifically names Ajay Narottam Shah, an academic who has worked exceedingly closely with the Finance Ministry, the NSE top brass and SEBI, for many years now.

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