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A place for redditors to discuss quantitative trading, statistical methods, econometrics, programming, implementation, automated strategies, and bounce ideas off each other for constructive criticism. Feel free to submit papers/links of things you find interesting.
Hi, I am looking for 5-minutes candle data (open-high-low-close-volume) feed for forex currency pairs (and if possible gold too). No trading needed, only data feed. It can be real time or delayed data but not end of the day. It should be reachable with a php website that hosted in linux server. (So json,xml,csv etc..) http://www.barchartondemand.com/freemarketdataapi.php is an alternative but they only gives 24 hours delayed data. So there is no intraday data. Do you have any recommendations? I know free is nearly impossible but at least it has to be affordable.
Marketing Plan for a FinTech company, where to start?
I have 2 positions offered to me: A pharmaceutical company and a FinTech solution platform and even though I have not give a definite answer as of yet but Im leaning more towards the FinTech company.
Its a sector I have never ever dealt or have been in, so that will be a new adventure
The position comes with equity and a couple other perks (even though the basic salary isnt as high as I'd expect)
Q. I don't know where to start drafting a high-level strategy and then funneling it down. From my past experience all I can think about is inbound marketing strategy/Go-To-Market Strategy. Is there something I'm missing? They deal with: separately managed account (SMA) and exchange-traded funds (EFTs), CRM, liquidity connectors, forex data feed from news outlet (e.g. Associated Press), digital investor experience and a lot of other stuff that I didnt delve deep into. Where to start exactly? I'm not overwhelmed per-se , I literally just stepped into the market after 4 months of searching for a job so I'm a bit rusty on what to do exactly.
Factset: How You can Invest in Hedge Funds’ Biggest Investment Tl;dr FactSet is the most undervalued widespread SaaS/IT solution stock that exists If any of you have relevant experience or are friends with people in Investment Banking/other high finance, you know that Factset is the lifeblood of their financial analysis toolkit if and when it’s not Bloomberg, which isn’t even publicly traded. Factset has been around since 1978 and it’s considered a staple like Bloomberg in many wealth management firms, and it offers some of the easiest to access and understandable financial data so many newer firms focused less on trading are switching to Factset because it has a lot of the same data Bloomberg offers for half the cost. When it comes to modern financial data, Factset outcompetes Reuters and arguably Bloomberg as well due to their API services which makes Factset much more preferable for quantitative divisions of banks/hedge funds as API integration with Python/R is the most important factor for vast data lakes of financial data, this suggests Factset will be much more prepared for programming making its way into traditional finance fields. According to Factset, their mission for data delivery is to: “Integrate the data you need with your applications, web portals, and statistical packages. Whether you need market, company, or alternative data, FactSet flexible data delivery services give you normalized data through APIs and a direct delivery of local copies of standard data feeds. Our unique symbology links and aggregates a variety of content sources to ensure consistency, transparency, and data integrity across your business. Build financial models and power customized applications with FactSet APIs in our developer portal”. Their technical focus for their data delivery system alone should make it stand out compared to Bloomberg, whose UI is far more outdated and complex on top of not being as technically developed as Factset’s. Factset is the key provider of buy-side portfolio analysis for IBs, Hedge funds, and Private Equity firms, and it’s making its way into non-quantitative hedge funds as well because quantitative portfolio management makes automation of risk management and the application of portfolio theory so much easier, and to top it off, Factset’s scenario analysis and simulation is unique in its class. Factset also is able to automate trades based on individual manager risk tolerance and ML optimization for Forex trading as well. Not only does Factset provide solutions for financial companies, they are branching out to all corporations now and providing quantitative analytics for them in the areas of “corporate development, M&A, strategy, treasury, financial planning and analysis, and investor relations workflows”. Factset will eventually in my opinion reach out to Insurance Risk Management a lot more in the future as that’s a huge industry which has yet to see much automation of risk management yet, and with the field wide open, Factset will be the first to take advantage without a shadow of a doubt. So let’s dig into the company’s financials now: Their latest 8k filing reported the following: Revenue increased 2.6%, or $9.6 million, to $374.1 million compared with $364.5 million for the same period in fiscal 2019. The increase is primarily due to higher sales of analytics, content and technology solutions (CTS) and wealth management solutions. Annual Subscription Value (ASV) plus professional services was $1.52 billion at May 31, 2020, compared with $1.45 billion at May 31, 2019. The organic growth rate, which excludes the effects of acquisitions, dispositions, and foreign currency movements, was 5.0%. The primary contributors to this growth rate were higher sales in FactSet's wealth and research workflow solutions and a price increase in the Company's international region Adjusted operating margin improved to 35.5% compared with 34.0% in the prior year period primarily as a result of reduced employee-related operating expenses due to the coronavirus pandemic. Diluted earnings per share (EPS) increased 11.0% to $2.63 compared with $2.37 for the same period in fiscal 2019. Adjusted diluted EPS rose 9.2% to $2.86 compared with $2.62 in the prior year period primarily driven by an improvement in operating results. The Company’s effective tax rate for the third quarter decreased to 15.0% compared with 18.6% a year ago, primarily due to an income tax expense in the prior year related to finalizing the Company's tax returns with no similar event for the three months ended May 31, 2020. FactSet increased its quarterly dividend by $0.05 per share or 7% to $0.77 marking the fifteenth consecutive year the Company has increased dividends, highlighting its continued commitment to returning value to shareholders. As you can see, there’s not much of a negative sign in sight here. It makes sense considering how FactSet’s FCF has never slowed down: https://preview.redd.it/frmtdk8e9hk51.png?width=276&format=png&auto=webp&s=1c0ff12539e0b2f9dbfda13d0565c5ce2b6f8f1a https://preview.redd.it/6axdb6lh9hk51.png?width=593&format=png&auto=webp&s=9af1673272a5a2d8df28f60f4707e948a00e5ff1 FactSet’s annual subscriptions and professional services have made its way to foreign and developing markets, and many of them are opting for FactSet’s cheaper services to reduce costs and still get copious amounts of data and models to work with. Here’s what FactSet had to say regarding its competitive position within the market of providing financial data in its last 10k: “Despite competing products and services, we enjoy high barriers to entry and believe it would be difficult for another vendor to quickly replicate the extensive databases we currently offer. Through our in-depth analytics and client service, we believe we can offer clients a more comprehensive solution with one of the broadest sets of functionalities, through a desktop or mobile user interface or through a standardized or bespoke data feed.” And FactSet is confident that their ML services cannot be replaced by anybody else in the industry either: “In addition, our applications, including our client support and service offerings, are entrenched in the workflow of many financial professionals given the downloading functions and portfolio analysis/screening capabilities offered. We are entrusted with significant amounts of our clients' own proprietary data, including portfolio holdings. As a result, our products have become central to our clients’ investment analysis and decision-making.” (https://last10k.com/sec-filings/fds#link_fullReport), if you read the full report and compare it to the most recent 8K, you’ll find that the real expenses this quarter were far lower than expected by the last 10k as there was a lower than expected tax rate and a 3% increase in expected operating margin from the expected figure as well. The company also reports a 90% customer retention rate over 15 years, so you know that they’re not lying when they say the clients need them for all sorts of financial data whether it’s for M&A or wealth management and Equity analysis: https://www.investopedia.com/terms/f/factset.asp https://preview.redd.it/yo71y6qj9hk51.png?width=355&format=png&auto=webp&s=a9414bdaa03c06114ca052304a26fae2773c3e45 FactSet also has remarkably good cash conversion considering it’s a subscription based company, a company structure which usually takes on too much leverage. Speaking of leverage, FDS had taken on a lot of leverage in 2015: https://preview.redd.it/oxaa1wel9hk51.png?width=443&format=png&auto=webp&s=13d60d2518980360c403364f7150392ab83d07d7 So what’s that about? Why were FactSet’s long term debts at 0 and all of a sudden why’d the spike up? Well usually for a company that’s non-cyclical and has a well-established product (like FactSet) leverage can actually be good at amplifying returns, so FDS used this to their advantage and this was able to help the share’s price during 2015. Also, as you can see debt/ebitda is beginning a rapid decline anyway. This only adds to my theory that FactSet is trying to expand into new playing fields. FactSet obviously didn’t need the leverage to cover their normal costs, because they have always had consistently growing margins and revenue so the debt financing was only for the sake of financing growth. And this debt can be considered covered and paid off, considering the net income growth of 32% between 2018 and 2019 alone and the EPS growth of 33% https://preview.redd.it/e4trju3p9hk51.png?width=387&format=png&auto=webp&s=6f6bee15f836c47e73121054ec60459f147d353e EBITDA has virtually been exponential for FactSet for a while because of the bang-for-buck for their well-known product, but now as FactSet ventures into algorithmic trading and corporate development the scope for growth is broadly expanded. https://preview.redd.it/yl7f58tr9hk51.png?width=489&format=png&auto=webp&s=68906b9ecbcf6d886393c4ff40f81bdecab9e9fd P/E has declined in the past 2 years, making it a great time to buy. https://preview.redd.it/4mqw3t4t9hk51.png?width=445&format=png&auto=webp&s=e8d719f4913883b044c4150f11b8732e14797b6d Increasing ROE despite lowering of leverage post 2016 https://preview.redd.it/lt34avzu9hk51.png?width=441&format=png&auto=webp&s=f3742ed87cd1c2ccb7a3d3ee71ae8c7007313b2b Mountains of cash have been piling up in the coffers increasing chances of increased dividends for shareholders (imo dividend is too low right now, but increasing it will tempt more investors into it), and on top of that in the last 10k a large buyback expansion program was implemented for $210m worth of shares, which shows how confident they are in the company itself. https://preview.redd.it/fliirmpx9hk51.png?width=370&format=png&auto=webp&s=1216eddeadb4f84c8f4f48692a2f962ba2f1e848 SGA expense/Gross profit has been declining despite expansion of offices I’m a bit concerned about the skin in the game leadership has in this company, since very few executives/board members have significant holdings in the company, but the CEO himself is a FactSet veteran, and knows his way around the company. On top of that, Bloomberg remains king for trading and the fixed income security market, and Reuters beats out FactSet here as well. If FactSet really wants to increase cash flow sources, the expansion into insurance and corp dev has to be successful. Summary: FactSet has a lot of growth still left in its industry which is already fast-growing in and of itself, and it only has more potential at its current valuation. Earnings September 24th should be a massive beat due to investment banking demand and growth plus Hedge fund requirements for data and portfolio management hasn’t gone anywhere and has likely increased due to more market opportunities to buy-in. Calls have shitty greeks, but if you're ballsy October 450s LOL, I'm holding shares I’d say it’s a great long term investment, and it should at least be on your watchlist.
I'm apparently some kind of moron- I'm unable to figure out how to get set up with automated trading on interactive brokers. Currently I'm paper trading forex on OandA using some simple curl requests on a headless server. I'd like to start trading futures, and ibkr was the top recommendation. Goals: -When my own (already developed) system generates an entry or exit alert, execute a market order on a NASDAQ micro futures contact. -Do it on a headless Google cloud server From chatting with some folks, I've heard I need to use IB Gateway, and there may be a need to initially use GUI for authentication. That's fine, as long as GUI elements aren't needed later when it's running. I don't need any kind of data feed from IBKR, since I already have a system that generates alerts when I want it to. I just need the bare minimum to actually execute these market orders. I've signed up for IBKR and gotten my account approved and all that. Then I thought I'd go over to the education library and work through the TWS materials (just to get acquainted) and then go through the TWS programming Python course. At this point, the website wants me to register or login. I click the button that says I already have an ibkr account, it asks me to login, I do, then it takes me to the account page. Where is the course!?😵 After going through that loop every which way, I've given up on that for now and I'm here asking you for help. Anyone willing to give some guidance, either in this thread or through DM? I feel like what I want to do is extremely simple and I will be barely scratching the surface of what's available to me through IBKR. I just want to get started. Advice, resources, guides, and insults are all welcome! Thanks!
I’ve been looking for a broker that has an API for index futures and ideally also futures options. I’m looking to use the API to build a customized view of my risk based on balances, positions, and market conditions. Searching the algotrading sub I found many API-related posts, but then when I actually read them and their comments, I found they’re often lacking in real substance. It turns out many brokers or data services that have APIs don’t actually support index futures and options via the API, and instead they focus on equities, forex, or cypto. So here’s the list of what I’ve found so far. This isn’t a review of these brokers or APIs and note that I have a specific application in mind (index futures and futures options). Perhaps you’re looking for an API for equities, or you just want data and not a broker, in which case there may be a few options. Also, I’m based in the US so I didn’t really look for brokers or platforms outside the US. If you have experience with these APIs, please chime in with your thoughts. Also, I may have missed some brokers or platforms. If I did or if you see anything that needs correction please let me know.
Broker with a variety of platforms including CQG, Rithmic, TT, some with APIs
Wow, this list grew longer than I originally thought it would be. If you spot a mistake, please let me know and I’ll correct it. Edit: - added Lightspeed API - updated Dashprime to indicate some of the APIs available - added Medved Trader to table - added marketstack to table
I have used MetaTrader for forex in the past and Alpaca for equities, and would now like to get a little more serious. I am currently trying to choose between IBKR vs. TDA for trading equities, futures (including cryptocurrency), forex, options, and pretty much whatever I can, via API. I plan on building with Python. What matters most to me is that my orders execute reliably and quickly. Good fills, things get triggered, not eaten up by all the wtf? fees but I don't mind paying commission for quality, data feed quality, etc. If it gets a signal, I don't want to have to worry about my broker crapping on me. I do not care how hard it is to use or set up as long as I can count on it in the end. I will take the time to get it right and want to use it long-term for trading everything. I do not plan to use any UI much if at all, everything done through code. What has been your experience in using IBKR vs. TDA for your algorithmic trading?
Factset: How You can Invest in Hedge Funds’ Biggest Investment Tl;dr FactSet is the most undervalued widespread SaaS/IT solution stock that exists If any of you have relevant experience or are friends with people in Investment Banking/other high finance, you know that Factset is the lifeblood of their financial analysis toolkit if and when it’s not Bloomberg, which isn’t even publicly traded. Factset has been around since 1978 and it’s considered a staple like Bloomberg in many wealth management firms, and it offers some of the easiest to access and understandable financial data so many newer firms focused less on trading are switching to Factset because it has a lot of the same data Bloomberg offers for half the cost. When it comes to modern financial data, Factset outcompetes Reuters and arguably Bloomberg as well due to their API services which makes Factset much more preferable for quantitative divisions of banks/hedge funds as API integration with Python/R is the most important factor for vast data lakes of financial data, this suggests Factset will be much more prepared for programming making its way into traditional finance fields. According to Factset, their mission for data delivery is to: “Integrate the data you need with your applications, web portals, and statistical packages. Whether you need market, company, or alternative data, FactSet flexible data delivery services give you normalized data through APIs and a direct delivery of local copies of standard data feeds. Our unique symbology links and aggregates a variety of content sources to ensure consistency, transparency, and data integrity across your business. Build financial models and power customized applications with FactSet APIs in our developer portal”. Their technical focus for their data delivery system alone should make it stand out compared to Bloomberg, whose UI is far more outdated and complex on top of not being as technically developed as Factset’s. Factset is the key provider of buy-side portfolio analysis for IBs, Hedge funds, and Private Equity firms, and it’s making its way into non-quantitative hedge funds as well because quantitative portfolio management makes automation of risk management and the application of portfolio theory so much easier, and to top it off, Factset’s scenario analysis and simulation is unique in its class. Factset also is able to automate trades based on individual manager risk tolerance and ML optimization for Forex trading as well. Not only does Factset provide solutions for financial companies, they are branching out to all corporations now and providing quantitative analytics for them in the areas of “corporate development, M&A, strategy, treasury, financial planning and analysis, and investor relations workflows”. Factset will eventually in my opinion reach out to Insurance Risk Management a lot more in the future as that’s a huge industry which has yet to see much automation of risk management yet, and with the field wide open, Factset will be the first to take advantage without a shadow of a doubt. So let’s dig into the company’s financials now: Their latest 8k filing reported the following: Revenue increased 2.6%, or $9.6 million, to $374.1 million compared with $364.5 million for the same period in fiscal 2019. The increase is primarily due to higher sales of analytics, content and technology solutions (CTS) and wealth management solutions. Annual Subscription Value (ASV) plus professional services was $1.52 billion at May 31, 2020, compared with $1.45 billion at May 31, 2019. The organic growth rate, which excludes the effects of acquisitions, dispositions, and foreign currency movements, was 5.0%. The primary contributors to this growth rate were higher sales in FactSet's wealth and research workflow solutions and a price increase in the Company's international region Adjusted operating margin improved to 35.5% compared with 34.0% in the prior year period primarily as a result of reduced employee-related operating expenses due to the coronavirus pandemic. Diluted earnings per share (EPS) increased 11.0% to $2.63 compared with $2.37 for the same period in fiscal 2019. Adjusted diluted EPS rose 9.2% to $2.86 compared with $2.62 in the prior year period primarily driven by an improvement in operating results. The Company’s effective tax rate for the third quarter decreased to 15.0% compared with 18.6% a year ago, primarily due to an income tax expense in the prior year related to finalizing the Company's tax returns with no similar event for the three months ended May 31, 2020. FactSet increased its quarterly dividend by $0.05 per share or 7% to $0.77 marking the fifteenth consecutive year the Company has increased dividends, highlighting its continued commitment to returning value to shareholders. As you can see, there’s not much of a negative sign in sight here. It makes sense considering how FactSet’s FCF has never slowed down FactSet’s annual subscriptions and professional services have made its way to foreign and developing markets, and many of them are opting for FactSet’s cheaper services to reduce costs and still get copious amounts of data and models to work with. Here’s what FactSet had to say regarding its competitive position within the market of providing financial data in its last 10k: “Despite competing products and services, we enjoy high barriers to entry and believe it would be difficult for another vendor to quickly replicate the extensive databases we currently offer. Through our in-depth analytics and client service, we believe we can offer clients a more comprehensive solution with one of the broadest sets of functionalities, through a desktop or mobile user interface or through a standardized or bespoke data feed.” And FactSet is confident that their ML services cannot be replaced by anybody else in the industry either: “In addition, our applications, including our client support and service offerings, are entrenched in the workflow of many financial professionals given the downloading functions and portfolio analysis/screening capabilities offered. We are entrusted with significant amounts of our clients' own proprietary data, including portfolio holdings. As a result, our products have become central to our clients’ investment analysis and decision-making.” (https://last10k.com/sec-filings/fds#link_fullReport), if you read the full report and compare it to the most recent 8K, you’ll find that the real expenses this quarter were far lower than expected by the last 10k as there was a lower than expected tax rate and a 3% increase in expected operating margin from the expected figure as well. The company also reports a 90% customer retention rate over 15 years, so you know that they’re not lying when they say the clients need them for all sorts of financial data whether it’s for M&A or wealth management and Equity analysis: https://www.investopedia.com/terms/f/factset.asp FactSet also has remarkably good cash conversion considering it’s a subscription based company, a company structure which usually takes on too much leverage. Speaking of leverage, FDS had taken on a lot of leverage in 2015: So what’s that about? Why were FactSet’s long term debts at 0 and all of a sudden why’d the spike up? Well usually for a company that’s non-cyclical and has a well-established product (like FactSet) leverage can actually be good at amplifying returns, so FDS used this to their advantage and this was able to help the share’s price during 2015. Also, as you can see debt/ebitda is beginning a rapid decline anyway. This only adds to my theory that FactSet is trying to expand into new playing fields. FactSet obviously didn’t need the leverage to cover their normal costs, because they have always had consistently growing margins and revenue so the debt financing was only for the sake of financing growth. And this debt can be considered covered and paid off, considering the net income growth of 32% between 2018 and 2019 alone and the EPS growth of 33% EBITDA has virtually been exponential for FactSet for a while because of the bang-for-buck for their well-known product, but now as FactSet ventures into algorithmic trading and corporate development the scope for growth is broadly expanded. P/E has declined in the past 2 years, making it a great time to buy. Increasing ROE despite lowering of leverage post 2016 Mountains of cash have been piling up in the coffers increasing chances of increased dividends for shareholders (imo dividend is too low right now, but increasing it will tempt more investors into it), and on top of that in the last 10k a large buyback expansion program was implemented for $210m worth of shares, which shows how confident they are in the company itself. SGA expense/Gross profit has been declining despite expansion of offices I’m a bit concerned about the skin in the game leadership has in this company, since very few executives/board members have significant holdings in the company, but the CEO himself is a FactSet veteran, and knows his way around the company. On top of that, Bloomberg remains king for trading and the fixed income security market, and Reuters beats out FactSet here as well. If FactSet really wants to increase cash flow sources, the expansion into insurance and corp dev has to be successful. Summary: FactSet has a lot of growth still left in its industry which is already fast-growing in and of itself, and it only has more potential at its current valuation. Earnings September 24th should be a massive beat due to investment banking demand and growth plus Hedge fund requirements for data and portfolio management hasn’t gone anywhere and has likely increased due to more market opportunities to buy-in. Calls have shitty greeks, but if you're ballsy October 450s LOL, I'm holding shares I’d say it’s a great long term investment, and it should at least be on your watchlist.
Factset: How You can Invest in Hedge Funds’ Biggest Investment Tl;dr FactSet is the most undervalued widespread SaaS/IT solution stock that exists If any of you have relevant experience or are friends with people in Investment Banking/other high finance, you know that Factset is the lifeblood of their financial analysis toolkit if and when it’s not Bloomberg, which isn’t even publicly traded. Factset has been around since 1978 and it’s considered a staple like Bloomberg in many wealth management firms, and it offers some of the easiest to access and understandable financial data so many newer firms focused less on trading are switching to Factset because it has a lot of the same data Bloomberg offers for half the cost. When it comes to modern financial data, Factset outcompetes Reuters and arguably Bloomberg as well due to their API services which makes Factset much more preferable for quantitative divisions of banks/hedge funds as API integration with Python/R is the most important factor for vast data lakes of financial data, this suggests Factset will be much more prepared for programming making its way into traditional finance fields. According to Factset, their mission for data delivery is to: “Integrate the data you need with your applications, web portals, and statistical packages. Whether you need market, company, or alternative data, FactSet flexible data delivery services give you normalized data through APIs and a direct delivery of local copies of standard data feeds. Our unique symbology links and aggregates a variety of content sources to ensure consistency, transparency, and data integrity across your business. Build financial models and power customized applications with FactSet APIs in our developer portal”. Their technical focus for their data delivery system alone should make it stand out compared to Bloomberg, whose UI is far more outdated and complex on top of not being as technically developed as Factset’s. Factset is the key provider of buy-side portfolio analysis for IBs, Hedge funds, and Private Equity firms, and it’s making its way into non-quantitative hedge funds as well because quantitative portfolio management makes automation of risk management and the application of portfolio theory so much easier, and to top it off, Factset’s scenario analysis and simulation is unique in its class. Factset also is able to automate trades based on individual manager risk tolerance and ML optimization for Forex trading as well. Not only does Factset provide solutions for financial companies, they are branching out to all corporations now and providing quantitative analytics for them in the areas of “corporate development, M&A, strategy, treasury, financial planning and analysis, and investor relations workflows”. Factset will eventually in my opinion reach out to Insurance Risk Management a lot more in the future as that’s a huge industry which has yet to see much automation of risk management yet, and with the field wide open, Factset will be the first to take advantage without a shadow of a doubt. So let’s dig into the company’s financials now: Their latest 8k filing reported the following: Revenue increased 2.6%, or $9.6 million, to $374.1 million compared with $364.5 million for the same period in fiscal 2019. The increase is primarily due to higher sales of analytics, content and technology solutions (CTS) and wealth management solutions. Annual Subscription Value (ASV) plus professional services was $1.52 billion at May 31, 2020, compared with $1.45 billion at May 31, 2019. The organic growth rate, which excludes the effects of acquisitions, dispositions, and foreign currency movements, was 5.0%. The primary contributors to this growth rate were higher sales in FactSet's wealth and research workflow solutions and a price increase in the Company's international region Adjusted operating margin improved to 35.5% compared with 34.0% in the prior year period primarily as a result of reduced employee-related operating expenses due to the coronavirus pandemic. Diluted earnings per share (EPS) increased 11.0% to $2.63 compared with $2.37 for the same period in fiscal 2019. Adjusted diluted EPS rose 9.2% to $2.86 compared with $2.62 in the prior year period primarily driven by an improvement in operating results. The Company’s effective tax rate for the third quarter decreased to 15.0% compared with 18.6% a year ago, primarily due to an income tax expense in the prior year related to finalizing the Company's tax returns with no similar event for the three months ended May 31, 2020. FactSet increased its quarterly dividend by $0.05 per share or 7% to $0.77 marking the fifteenth consecutive year the Company has increased dividends, highlighting its continued commitment to returning value to shareholders. As you can see, there’s not much of a negative sign in sight here. It makes sense considering how FactSet’s FCF has never slowed down FactSet’s annual subscriptions and professional services have made its way to foreign and developing markets, and many of them are opting for FactSet’s cheaper services to reduce costs and still get copious amounts of data and models to work with. Here’s what FactSet had to say regarding its competitive position within the market of providing financial data in its last 10k: “Despite competing products and services, we enjoy high barriers to entry and believe it would be difficult for another vendor to quickly replicate the extensive databases we currently offer. Through our in-depth analytics and client service, we believe we can offer clients a more comprehensive solution with one of the broadest sets of functionalities, through a desktop or mobile user interface or through a standardized or bespoke data feed.” And FactSet is confident that their ML services cannot be replaced by anybody else in the industry either: “In addition, our applications, including our client support and service offerings, are entrenched in the workflow of many financial professionals given the downloading functions and portfolio analysis/screening capabilities offered. We are entrusted with significant amounts of our clients' own proprietary data, including portfolio holdings. As a result, our products have become central to our clients’ investment analysis and decision-making.” (https://last10k.com/sec-filings/fds#link_fullReport), if you read the full report and compare it to the most recent 8K, you’ll find that the real expenses this quarter were far lower than expected by the last 10k as there was a lower than expected tax rate and a 3% increase in expected operating margin from the expected figure as well. The company also reports a 90% customer retention rate over 15 years, so you know that they’re not lying when they say the clients need them for all sorts of financial data whether it’s for M&A or wealth management and Equity analysis: https://www.investopedia.com/terms/f/factset.asp FactSet also has remarkably good cash conversion considering it’s a subscription based company, a company structure which usually takes on too much leverage. Speaking of leverage, FDS had taken on a lot of leverage in 2015: So what’s that about? Why were FactSet’s long term debts at 0 and all of a sudden why’d the spike up? Well usually for a company that’s non-cyclical and has a well-established product (like FactSet) leverage can actually be good at amplifying returns, so FDS used this to their advantage and this was able to help the share’s price during 2015. Also, as you can see debt/ebitda is beginning a rapid decline anyway. This only adds to my theory that FactSet is trying to expand into new playing fields. FactSet obviously didn’t need the leverage to cover their normal costs, because they have always had consistently growing margins and revenue so the debt financing was only for the sake of financing growth. And this debt can be considered covered and paid off, considering the net income growth of 32% between 2018 and 2019 alone and the EPS growth of 33% EBITDA has virtually been exponential for FactSet for a while because of the bang-for-buck for their well-known product, but now as FactSet ventures into algorithmic trading and corporate development the scope for growth is broadly expanded. P/E has declined in the past 2 years, making it a great time to buy. Increasing ROE despite lowering of leverage post 2016 Mountains of cash have been piling up in the coffers increasing chances of increased dividends for shareholders (imo dividend is too low right now, but increasing it will tempt more investors into it), and on top of that in the last 10k a large buyback expansion program was implemented for $210m worth of shares, which shows how confident they are in the company itself. SGA expense/Gross profit has been declining despite expansion of offices I’m a bit concerned about the skin in the game leadership has in this company, since very few executives/board members have significant holdings in the company, but the CEO himself is a FactSet veteran, and knows his way around the company. On top of that, Bloomberg remains king for trading and the fixed income security market, and Reuters beats out FactSet here as well. If FactSet really wants to increase cash flow sources, the expansion into insurance and corp dev has to be successful. Summary: FactSet has a lot of growth still left in its industry which is already fast-growing in and of itself, and it only has more potential at its current valuation. Earnings September 24th should be a massive beat due to investment banking demand and growth plus Hedge fund requirements for data and portfolio management hasn’t gone anywhere and has likely increased due to more market opportunities to buy-in.
(Sadly this is apparently moot now since as soon as I ended the trial they yanked the early bird offer back; couldn't pay even if I wanted to.) I am (or was) trialing TradingView Premium which supposedly offers priority support, including by phone from what I've seen mentioned. Had some health+life issues and couldn't get working on my device. Filed a support ticket and reached out on Twitter as well, asking for a few more days so I could try out with live market data. Imagine my surprise not to hear back at all for multiple days. So I elected not to renew after the trial (with radio silence, didn't trust them not to bill at midnight UTC). Imagine my further surprise when it immediately deactivated every trial feature and, on top of that, yanked the early bird offer. Even if I click the "last chance - act now" button from another open window it redirects to the full price. If they'd replied, even with just the single word "no", I would likely go ahead and accept the early bird offer and sign up for Premium yearly + data feeds. (I'd signed up for another service that might offer me the same, but haven't been activated yet, so was just in a time as well as budget crunch.) But I'm surprised and disappointed in the level of service I would get even as a Premium user and they're not making the case, especially when one of the key use cases is forex/crypto/futures which do run on the weekend. And now they won't even take my money. It feels petty.
Weekly Wrap: This Week In Chainlink July 20 - July 26
Weekly Wrap: This Week In Chainlink July 20 - July 26. What a week! Check out this weekly wrap-up of all that has been accomplished in the Chainlink community.
SmartCon will feature the top minds and builders of smart contracts and celebrate our incredible community, thriving ecosystem & cutting-edge research. Experience a mix of keynotes, panel discussions, live demos, developer workshops, and networking with the community. We made registration complimentary so everyone can participate.
We’re thrilled to welcome DeutscheTelekom’s TSystems_MMS IT Services group to Chainlink. Tsystemscom’s world-class infrastructure team secures a large amount of enterprise value today & is now on mainnet helping secure Chainlink’s oracle network.
Top Korean banks: Hana Bank, Shinhan Bank, Nonghyup Bank, and Industrial Bank of Korea select Chainlink and CenterPrime to bring their forex data on-chain, transforming the capabilities of open banking services, fintech and DeFi.
Binance Smart Chain has integrated Chainlink as its oracle live on testnet! Using Chainlink gives devs access to off-chain data (e.g. Binance_DEX), enabling them to build DeFi dApps for derivatives, crypto payments, automated asset management and more.
Reflexer (@MetaCoinProject) has successfully integrated Chainlink's ETH/USD Price Reference Data as the basis for collateralization checks on their first Generalized Ethereum Bond (reflex bond) RAI—a low volatility, trust minimized collateral for DeFi.
Blockchain-based e-document solution @FirmaChain is integrating Chainlink to create more seamless digital contracts. For example, car rental contracts using Chainlink to validate driver licenses within the signature process for better customer experience.
Blockchain platform Elastos blockchain is launching a Chainlink-powered ELA/USD Price Reference Data feed to use for collateralization checks on its upcoming cross-chain stablecoin. This is one of many advanced dApps possible on Elastos using real-world data.
TinyboxesETH is using ChainlinkVRF to create Tiny Boxes, randomized & animated generative art pieces that, from creation to curation, exist fully on-chain for collectors to enjoy. They will also use Chainlink price oracles for minting pieces w/ crypto.
Chainlink's ENJ/ETH Price Reference Feed is live on mainnet! Gaming developers can use this reliable price feed when minting or exchanging Enjin-based digital assets.
Chainlink's REN/ETH Price Reference Feed is live on mainnet. DeFi developers utilizing REN in their dApp now have access to a secure and reliable price oracle. This is just one of many Chainlink oracles available today.
Join the MCDEX team and Chainlink for a video Q&A is with Gareth the DaoChemist (https://twitter.com/daochemist), Head of Business Development of MCDEX. The discussion will be centered on MCDEX integration with Chainlink and a deep-dive into MCDEX's launch of liquidity mining.
Join the Vite Labs team and Chainlink for a video Q&A is with Richard Yan, the Co-founder, and COO of Vite Labs. The discussion will be centered on 1) Why ViteX has better performance than other DEXs, 2) ViteX's approach to trans and liquidity mining where the coins earned entitle users to proceeds from the exchange, 3) Future plans for ViteX.
Watch this community workshop featuring an AMA with LinkPool’s head of business development, Ian Read. In the video, they discuss the future roadmap for LinkPool, how to become a node operator, and best practices for running a node.
Getting breaking news stories / events in real-time
Hi everyone: I'm trying to find a website that reports on big news stories in real-time and what is driving the markets in real-time. Case in point: today at ~2:50PM EST, news broke that the Moderna vaccine results have some problems with it. Stocks plunged between 2:55 PM and 3:00 PM. News sites reported on this vaccine news with 30-60 min lag, which is far too long if you're trying to daytrade aggressively.
The one website I've found so far that does a good job at this is ForexLive, who reported on this at 3:00 PM. Are there any other websites or Twitter feeds that work for this? Thanks!!
Hi, I'm new to daytrading and i have a few questions. I'm from the netherlands btw so not US based. 1. What is the best platform+broker for a +/-500,- account? I really dont want to spend too much on monthly costs and fee's, but i do need a good platform with level 2 quotes, real time america stocks data and customisable charts. If this has already been asked, can someone please send the link to the post?
What is the best way to papertrade on the stock market? (For stocks between 1-10 euro's) I tried some, but some platforms have 15m delay, and not reliable level 2 quotes. And others don't have the data feed i need. For example ninjatrader only has forex or future papertrading data feed. But I do like the platform.
Getting breaking news stories / events in real-time?
Hi everyone: I'm trying to find a website that reports on big news stories in real-time and what is driving the markets in real-time. Case in point: today at ~2:50PM EST, news broke that the Moderna vaccine results have some problems with it. Stocks plunged between 2:55 PM and 3:00 PM. News sites reported on this vaccine news with 30-60 min lag, which is far too long if you're trying to daytrade aggressively.
The one website I've found so far that does a good job at this is ForexLive, who reported on this at 3:00 PM. Are there any other websites or Twitter feeds that work for this? Thanks!!
I've reproduced 130+ research papers about "predicting the stock market", coded them from scratch and recorded the results. Here's what I've learnt.
ok, so firstly, all of the papers I found through Google search and Google scholar. Google scholar doesn't actually have every research paper so you need to use both together to find them all. They were all found by using phrases like "predict stock market" or "predict forex" or "predict bitcoin" and terms related to those. Next, I only tested papers written in the past 8 years or so, I think anything older is just going to be heavily Alpha-mined so we can probably just ignore those ones altogether. Then, Anything where it's slightly ambiguous with methodology, I tried every possible permutation to try and capture what the authors may have meant. For example, one paper adds engineered features to the price then says "then we ran the data through our model" - it's not clear if it means the original data or the engineered data, so I tried both ways. This happens more than you'd think! THEN, Anything that didn't work, I tried my own ideas with the data they were using or substituted one of their models with others that I knew of. Now before we go any further, I should caveat that I was a profitable trader at multiple Tier-1 US banks so I can say with confidence that I made a decent attempt of building whatever the author was trying to get at. Oh, and one more thing. All of this work took about 7 months in total. Right, let's jump in. So with the papers, I found as many as I could, then I read through them and put them in categories and then tested each category at a time because a lot of papers were kinda saying the same things. Here are the categories:
News Text Mining. - This is where they'd use NLP on headlines or the body of news as a signal.
Social data - Twitter Sentiment/Google Search/Seeking Alpha. Again, some were NLP, for google trends they just used the data.
Technical Analysis & Machine Learning together. Most of these would take the price, add TA features, then feed into a ML model.
Other machine learning (as in, not using TA). Just using the price and some other engineered features.
Analyst Recommendations. Literally just taking the recommendations from banks/brokers and using that as the signal.
Fundamental data. So ratios from the income statement/balance sheet,
Results: Literally every single paper was either p-hacked, overfit, or a subsample of favourable data was selected (I guess ultimately they're all the same thing but still) OR a few may have had a smidge of Alpha but as soon as you add transaction costs it all disappears. Every author that's been publicly challenged about the results of their paper says it's stopped working due to "Alpha decay" because they made their methodology public. The easiest way to test whether it was truly Alpha decay or just overfitting by the authors is just to reproduce the paper then go further back in time instead of further forwards. For the papers that I could reproduce, all of them failed regardless of whether you go back or forwards. :) Now, results from the two most popular categories were:
*Social data.*A lot of research papers were extensions of or based off of a paper by Johan Bollen called "Twitter mood predicts the stock market". It literally has 3,955 citations and is complete and utter horse shit; the paper is p-hacking to the extreme. Not only could I not reproduce the results, but given the number of sentiment indicators he uses I regularly found correlations between sentiment and my data based on how I engineered it. None of these correlations held over longer time periods. Every paper that's a derivative of this one or cites it has the same issues.
*Technical analysis & machine learning.*Every paper would do something along the lines of.. take past price data for some asset (stocks, forex), then add technical analysis indicators as "features". Then either they'd run through a feature-selector that figures out the best features then put the best ones into a model OR they'd dump this data straight into the model and afterwards select the subset of instruments that it "worked" on. None of these would hold if you k-fold test them or test on different subsets of data outside of the ones used in the paper. The results are always based off of selecting favourable subsets of data.
The most frustrating paper: I have true hate for the authors of this paper: "A deep learning framework for financial time series using stacked autoencoders and long-short term memory". Probably the most complex AND vague in terms of methodology and after weeks trying to reproduce their results (and failing) I figured out that they were leaking future data into their training set (this also happens more than you'd think). The two positive take-aways that I did find from all of this research are:
Almost every instrument is mean-reverting on short timelines and trending on longer timelines. This has held true across most of the data that I tested. Putting this information into a strategy would be rather easy and straightforward (although you have no guarantee that it'll continue to work in future).
When we were in the depths of the great recession, almost every signal was bearish (seeking alpha contributors, news, google trends). If this holds in the next recession, just using this data alone would give you a strategy that vastly outperforms the index across long time periods.
Hopefully if anyone is getting into this space this will save you an absolute tonne of time and effort. So in conclusion, if you're building trading strategies. Simple is good :) Also one other thing I'd like to add, even the Godfather of value investing, the late Benjamin Graham (Warren Buffet's mentor) used to test his strategies (even though he'd be trading manually) so literally every investor needs to backtest regardless of if you're day-trading or long-term investing or building trading algorithms.
Hi, I am developing a Forex algo and have done so with 1-minute candle data. For simplicity sake I take the average (mid-price) of the bid/ask-close, bid/ask-low and bid/ask-high and feed these into the algo. This means that the algo buys and sells using the bid/ask-close average. Now, I know this ignores the "cost" of the spread and since it is a HF algo this cost becomes very significant. However, I recently found out about ECN brokerages that say they offer 0-0.1pips of spread and only take a commission. My question thus is, what kind of data should I use for backtesting my algo. The candle data I use now has an average spread of 1.4 pips (between bid-close and ask-close). Should I find data quoting a lower spread? Or are these lower spreads not realistic? In addition to the question above... Is using the bid-close and ask-close of 1-minute candle date reasonable to use as a price at which the algo can buy and sell or is this not realistic in a live environment. Thank you so much
Post any options questions you wanted to ask, but were afraid to ask. A weekly thread in which questions will be received with equanimity. There are no stupid questions, only dumb answers.Fire away. This is a weekly rotation with past threads linked below. This project succeeds thanks to people thoughtfully sharing their knowledge and experiences (YOU are invited to respond to questions posted here.) Perhaps you're looking for an item in the frequent answers list below. For a useful response about a particular option trade, disclose position details, so that responders can assist. Vague inquires receive vague responses. Tell us: TICKER -- Put or Call -- strike price (for each leg, on spreads) -- expiration date -- cost of option entry -- date of option entry -- underlying stock price at entry -- current option (spread) market value -- current underlying stock price -- your rationale for entering the position. . Key informational links: • Glossary • List of Recommended Books • Introduction to Options (The Options Playbook) • The complete side-bar informational links, for mobile app users.
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How to normalize values with no bounds, mean or standard deviation
I am writing a feed forward neural network that uses NEAT to learn to trade in the forex market. I made another post on here asking about activation functions and I got a lot of awesome feedback, but now I am puzzling over how to normalize my input data properly for optimal performance. I have a lot of different input data and I have no good way to standardize them. Things like price data have no constant bounds or statistical data and things like account balance could be any number from 0 to infinity. I could use bounding functions like logit but I'm not sure if that is the best idea. What do you guys recommend when you have boundless inputs with no mean or stddev? and is normalization really necessary in this case? I was considering using selu as my activation function, but selu might not be the best choice in this scenario, as it requires inputs to be standardized to mean 0 stddev 1. Thanks!
[D] How to normalize values with no bounds, mean or standard deviation
I am writing a feed forward neural network that uses NEAT to learn to trade in the forex market. I made another post on here asking about activation functions and I got a lot of awesome feedback, but now I am puzzling over how to normalize my input data properly for optimal performance. I have a lot of different input data and I have no good way to standardize them. Things like price data have no constant bounds or statistical data and things like account balance could be any number from 0 to infinity. I could use bounding functions like logit but I'm not sure if that is the best idea. What do you guys recommend when you have boundless inputs with no mean or stddev? and is normalization really necessary in this case? I was considering using selu as my activation function, but selu might not be the best choice in this scenario, as it requires inputs to be standardized to mean 0 stddev 1. Thanks!
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