# Tag Info

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PyInvesting allows you to backtest your investment strategy without writing a single line of code. Simply fill in a form specifying your backtest details Create signals using both technical and fundamental data Backtest your prefered investment strategy (Relative Strength, Fundamentals, Moving Average and Strategic Allocation) Performance analysis is a ...

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let's work through an example. Daimler equity has isin DE0007100000 Going to https://bsym.bloomberg.com/ , we find German ticker symbol DAI. (there is also an API, I believe free) We can find on Yahoo Finance https://finance.yahoo.com/quote/DAI.DE

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Just use the symbol (in the CSV files, this is in the "Mnemonic" column) and attach an .F suffix for Frankfurt Boerse. More information. Note: Since scraping the API is expensive (although quotes can be retrieved in large batches of up to 500 symbols), you typically want to restrict the lookups to the symbols which you can trade with your broker, ...

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Yes, I have a database that covers all 70,000 stocks worldwide. You can download the European portion from the original table hosted on this Public Domain. The tickers comply to Yahoo Finance's version.

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Hi you can use Alphavantage and inquire a student offer, that gives you more calls per minute and more calls per month. You only have to have a student mail account and confirm the purposes of your endeavour.

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Sniffing (or stalking) algo indeed detects other algorithms. How does that work in practice? Imagine the order book for a particular equity is: Bid 1 = 99 (size 10,000), Bid 2 = 98 (size 25,000), Bid 3 = 97 (size 30,000), Offer 1 = 101 (size 10,000), Offer 2 = 102 (size 25,000), Offer 3 = 103 (size 30,000). So in the example above, the bids and offers are ...

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Since 2008, we have the longest bull market ever on the record due to the adoption of Quantitative Easing, low-interest rates, and easy monetary policy by most countries of the world. The utility sector is a defensive sector mainly because of predictable growth and cash flow. During this long bull market, the utility sector had struggled to keep pace with ...

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Depends on what factors you are considering. Things like momentum can be calibrated daily. For Factors like value, it may be a good idea to calibrate monthly.

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I think I understand your issue. Your weights are not uniformly distributed over the desired space. Instead you weights are bunched together, there are fewer extreme weights (weights close to 1) than there should be. For example let's consider how often the 1st weight (or any other weight for that matter) should be between 0.8 and 1.0. Since the length of ...

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It's normal that it takes very long to come close to the efficient frontier with random portfolios. How close you come how fast will be strongly influenced by how you sample the portfolios. In your code, you sample uniformly. You may want to look at the weight distributions of the portfolios on the frontier, and then consider how likely it is that you arrive ...

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Not sure why you would seek to hit this via trial-and-error sampling in the first place. By definition, the MinVar portfolio is the tangency / MaxSharpe portfolio if all the returns are assumed to be constant/equal. See pp.7-10 in e.g. https://faculty.washington.edu/ezivot/econ424/portfolioTheoryMatrix.pdf

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You can find intraday data at http://investing.com. You can use the investpy Python package to pull the data: https://pypi.org/project/investpy/

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Why did you expect to hit the minimum variance portfolio in your simulations? There are infinitely many vectors of size Nx1, such that the sum of its elements adds up to 1. Even with 1 million simulations, it is very difficult to hit the exact minimum variance allocation vector. Moreover, your simulations might have hit some allocation vector "very near&...

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It's a momentum strategy. I would implement it as a staged pipeline: Initialize an instrument watch list prior to trading session. It can be a black/white list based on risk parameters and/or reference data. For example, only US common stocks (no ADRs, ETFs, etc.) with a market cap of over \$1B. Load all-time-high (ATH) prices and ATH dates for the ...

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I really suggest buying the data (from the vendors listed on the megathread What data sources are available online?, or mine https://tendollardata.com/product/historical-minute-data-historical-intraday-data-dataset-all-us-equities-etfs-sp-500-tech-stocks/). I wasted years of my life extracting data. It's not worth the upkeep. If you really are allergic to ...

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There are many (hundreds?) of ways to get historical equity data online, both free and paid. Check out the "What data sources are available online?" megathread: What data sources are available online? I have my own data service I just started, https://tendollardata.com, that I really do think is worth checking. I write out why in that thread (https:...

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What works for me is choosing each sector for a specific country manually, through the Excel add-in of Eikon (Datasteam -> Series requests -> Time series request). A window pops-up, that asks for the Series/list and what datatype you want. You click the orange box find series. Then you have to find through the search bar each sector in China. For ...

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DCF analysis: future values of dividends discounted to today's dollars Comparable company analysis: relative value based on peer group ratios: P/E, P/sales, P/active_user, P/EV, P/TAM for SPACs or ventures Terminal value: total value of assets sold minus liabilities paid For more details, see Company Valuation Methods by Pablo Fernandez

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The building blocks of this BRC are (assuming the most common specification, there is always variations but it's impossible to tell the details without any termsheet provided): long a zero-coupon bond short a down-and-in rainbow put on the min (worst-of), with barrier below strike usually, and either European or American exercise style The discount of the ...

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Here are some alternatives: Utilize Accumulate Distribute algorithm to reduce market impact. Place an iceberg order. Place a LOC (limit on close) order to execute it at the closing cross.

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Fix the RNG (random number generator)'s seed. This article from sharpsightlabs.com is accessible and illuminating.

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It only means that the directions of future movements of stock prices are impossible to forecast. For a more mathematical explanation, consider this document.

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I think the author is just saying that $$\begin{split} \frac{S - e^{-r}S_d}{S_u - S_d} S_u + \frac{e^{-r}S_u - S}{S_u - S_d} S_d &= \frac{S S_u - e^{-r}S_d S_u + e^{-r}S_uS_d - SS_d}{S_u - S_d} \\ &= \frac{S (S_u - S_d)}{S_u - S_d} \\ &= S \end{split}$$ and that \begin{split} \frac{S - e^{-r}S_d}{...

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