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Quantitative

New Strategy Testing Consolidation HypothesisIf you see previous trades in this account, you'll notice this strategy has never been used before. This strategy consists of new indicators I created through my own research and back tested it using Yahoo Finance Data. Today I finally coded the indicators into tradingview, however I will not be sharing the code. Basically, the indicator is reading the trend as it currently is. Determining it's a bullish trend if the blue line is above 0 and the opposite is true. Once the blue line reaches the limits, then it's considered a local minimum or maximum. These however are not always activated, so it's up to the user to determine if the movement is way too close to the limit and therefore should close the position. However, it can also be possible that a strong trend causes many consecutive maximums to appear. It's up to the user to determine if the maximums are just part of a strong trend or actually a maximum. This exception happens more on the upside than the downside, making minimum signals more reliable. Looking at how the SPX has behaved and seeing it come out of a slump and with the elections coming up it would seem reasonable to see investors skeptical about the future. Whoever wins the election will have a heavy impact on the price action, however, I doubt investors will make up their mind until then. Therefore, it's reasonable to assume the market may stall before continuing its growth. Also allowing the technicals to reset for a healthy bullish setup for the long term. Warning: This is the first live test of this strategy!! Estimate time for price to increase : 1months-6 months Expect price to stall within the drawn range. For the following weeks I don't expect any mayor price movements until the elections, unless a sudden international event happens.
CME_MINI:ES1!Long
by DarkMessiah777
Quantitative Finance: Mathematical and Financial ConceptsWhat is Quantitative Trading? The first step in understanding quantitative trading is understanding its definition. It's a type of trading that uses mathematical models to make transaction decisions. This is an extremely math-heavy field of trading and that is what makes it so effective. Traders then input certain parameters into their model, which then uses these parameters to make decisions based on mathematical calculations. Quantitative trading heavily relies on mathematical and statistical concepts. Here are some of the key types of math involved in quantitative trading: 1. Calculus: Calculus, especially differential calculus, is used to optimize trading strategies, calculate the sensitivity of prices to various factors (like in the Greeks of options pricing), and model the expected change in different variables. 2. Linear Algebra: Linear algebra is used in various areas of quantitative finance, including portfolio optimization, risk management, and structuring of derivatives. Machine learning algorithms, which are frequently used in quantitative trading, often rely on linear algebra as well. 3. Probability Theory and Statistics: These are fundamental to quant trading. They're used to create statistical models of market behavior, estimate the likelihood of different outcomes, evaluate the risk and return of different strategies, and test the effectiveness of different trading models. Concepts like probability distributions, regression analysis, hypothesis testing, correlation, and covariance are all crucial. 4. Time Series Analysis: This is a specialized field of statistics that deals with data points ordered in time. Financial data, like stock prices or trading volumes, are time series, so time series analysis is used to identify trends, cycles, or other patterns in the data, and to forecast future values. 5. Stochastic Calculus: This branch of mathematics is used to model random processes, like the movement of stock prices. It's fundamental to the pricing of derivatives, like options, and is used in risk management and portfolio optimization. 6. Optimization: This involves finding the best solution (maximizing or minimizing a function) given certain constraints, like finding the portfolio with the highest expected return for a given level of risk. Optimization techniques are heavily used in portfolio construction, risk management, and algorithm design. 7. Numerical Methods: These are techniques used to find numerical solutions to mathematical problems. For example, they're used in options pricing, where we often need to find numerical solutions to partial differential equations. 8. Machine Learning and Data Mining: While not strictly a branch of mathematics, these disciplines heavily rely on mathematical techniques and are used to analyze large datasets, make predictions, and develop trading strategies. A strong understanding of these mathematical fields is crucial for anyone considering a career in quantitative trading. However, it's also important to have a strong understanding of finance and economic principles, as these provide the context in which the math is applied. Here are some of the key financial and economic principles you need to know: 1. Financial Markets: Understanding how different markets operate is key. This includes knowledge of the stock market, forex market, futures market, options market, bond market, and commodities market. You should understand how these markets function, what drives price changes, and how different events can impact the markets. 2. Financial Instruments: This includes understanding different financial instruments like stocks, bonds, futures, options, swaps, and other derivatives. Each of these has its own characteristics and dynamics. 3. Risk and Return: An understanding of the risk-return tradeoff is crucial. This includes understanding how to measure risk (e.g., standard deviation, Value at Risk, etc.) and return (e.g., mean return, Sharpe ratio, etc.) and how to optimize the risk-return tradeoff (e.g., portfolio optimization). 4. Financial Statements and Ratio Analysis: While this is more important for strategies that use fundamental data, understanding financial statements (balance sheet, income statement, cash flow statement) and how to calculate and interpret financial ratios can be helpful. 5. Economic Indicators: Understanding various economic indicators like GDP, inflation, interest rates, unemployment rate, consumer sentiment, etc., and their impact on financial markets is important, especially for strategies that trade based on macroeconomic data. 6. Behavioral Finance: This involves understanding the psychological factors that affect market participants and can lead to various market anomalies. 7. Portfolio Theory: This includes understanding concepts like diversification, the efficient frontier, the Capital Asset Pricing Model (CAPM), and the concept of beta. 8. Derivatives Pricing Models: Understanding models like Black-Scholes for options pricing, or the concept of no-arbitrage pricing, can be very useful for strategies that involve derivatives. 9. Interest Rates and Fixed Income: Understanding the dynamics of interest rates, the term structure, yield curves, and how to price fixed income securities is crucial for strategies that involve bonds or interest rate derivatives. 10. Market Microstructure: This involves understanding how trades are executed in the market, what factors determine the bid-ask spread, what causes price impact, and other nuances of how trading actually works. These are just some of the many financial and economic concepts that can be important in quantitative finance. The specifics will depend on what type of strategies you are interested in (e.g., high-frequency trading vs. long-term asset allocation, equities vs. fixed income, etc.). In conclusion, delving into the world of quantitative finance requires a solid understanding of various mathematical and financial concepts. From probability and statistics to calculus, linear algebra, and optimization techniques, each piece of knowledge plays a crucial role in analyzing financial data, managing risk, and developing effective trading strategies. By mastering these essential mathematical tools, you gain a competitive edge over the majority of traders. While algorithmic trading can be quite challenging at times, it is extremely reliable and effective and I suggest every serious trader learns about it and how it works. Hope this helped!
AMEX:SPYEducation
by BiggWigg
USDJPY (Hedge Idea) With all financial markets preparing for the upcoming summer rate hikes, I predict markets will consolidate within a larger than usual range presenting great opportunities for investments. Next Hike: June 15-16, 2022. Hedge Idea (Scale / Intraday): Short: Scale into positions when price breaches 130.000 handle up to the top third end of the range (131.500) Long: Scale into positions when price breaches 128.250 handle & below to the bottom end of the range (127.000) POST FOMC HIKES (Mid-Term Forecast): LONG Target Price: 140.000 Target Date: End of July / Beginning of August
by brandonmattis
How much of the Japanese stock market does the BOJ own?The Bank of Japan (BOJ), unlike any of its peers, has become a huge player in the country’s stock market. What began as a monetary policy experiment has turned into what some economists describe as a caveat for policymakers about the extent of intervention a central bank may take in propping up capital markets. Over the past decade, the BOJ managed to gobble up 80% of Japan’s exchange-traded funds (ETFs), accounting for about 7% of the country’s $6 trillion stock market, according to Bloomberg. Based on the Government Pension Investment Fund’s annual report for fiscal 2020 ended March 2021, the government held more than 47 trillion yen worth of Japanese stocks. GPIF is Japan’s largest public fund investor by assets. While ETFs in other parts of the world are used to monitor the performance of certain stocks according to industries, Japan has used its ETF investments to control inflation with the goal of spurring economic growth. The BOJ started employing this strategy in the later part of 2010 when it began acquiring shares listed on Japanese exchanges via ETFs as part of its quantitative and qualitative easing program. The program to buy ETFs began as a part of the central bank’s purchase of Japanese government bonds, until the BOJ tested stock-fund buying, hoping to boost stock prices, which in turn encouraged companies to spend more on expansions, create more jobs and push inflation higher. However, six years into the ETF-buying program, the BOJ still wasn’t able to reach its inflation target, prompting Governor Haruhiko Kuroda to introduce negative interest rates to prevent a strong yen that was hurting the country’s export-heavy economy. As it stands, the Japanese yen is trading at 130 per USD, a 20-year low for the currency, and could be heading for weaker territory without intervention. While a weaker yen has been welcomed by Kuroda, Reuters reported that Japan could be considering currency intervention to stem further weakness in the yen. The Reuters report helped the USDJPY push above a month’s long resistance of 129 per USD. Aside from stocks, the BOJ has also racked up large amounts of Japanese government bonds totaling 521 trillion yen as of the end of 2021. The level of bond holdings, however, has fallen for the first time in 13 years as the BOJ sought to taper its bond-buying program due to concerns of a looming financial risk. Where to from here? Fast forward to 2022, the BOJ is still stuck with a huge amount of bonds and stocks that the central bank may not be able to easily decrease as a sell-off would have adverse effects on the country’s capital markets. “The bank was surrounded by dead ends. They were cornered into a place where they couldn’t do anything else,” Izuru Kato, president at Totan Research, was quoted by Bloomberg as saying. Back in 2019, Kuroda defended the BOJ’s ETF-buying program, dismissing concerns that it is distorting influence. "At present, I don’t think our ETF buying is having any effect on market function… But we continue to watch out to make sure there are no negative side effects,” Kuroda was quoted by the Financial Times as saying. Most recently in March, as concerns over its stock holdings grew, the BOJ governor said it was premature to debate an exit from quantitative easing including how the central bank could pare its ETF holdings as inflation has yet to sustainably hit 2%. Kuroda had also hinted that in the event the BOJ decides to wind down its stock holdings, it will employ a strategy that would minimize the BOJ’s losses and any financial market disruption. "They cannot sell now. Shares will fall for sure... The negative impact would be pretty huge,” Tetsuo Seshimo, portfolio manager at Saison Asset Management, said earlier this month.
by BlackBull_Markets
Your FAQ About Quant Trading I have received many questions about quantitative analysis/quant trading. This post is to address these FAQ I receive and point you in the right direction if this is something you are interested in! If I missed any questions, please leave them in the comments and I will add an addendum! Q: What is quantitative analysis/quant data? A : Quantitative analysis is the practice of applying mathematics and statistics to stock trading data. It involves the process of data mining and drawing statistic inferences between related and unrelated variables to look for correlations in data that can be used to predict future stock movement. Q: What is a “quant”? A: There are two types of quants or quant traders. This is more applicable to hedge funds and banks who employ these people, but essentially, there are quantitative modellers and quant developers/programmers. Quantitative modellers (which is essentially, what I am) are generally statisticians who have a degree in applied mathematics or statistics. They employ statistical theories to develop working mathematical models of stocks and attempt to quantify stock behaviour into mathematical formulas and determine probability of meeting certain conditions (i.e. price). Quant developers/programmers generally have degrees in computer science or computer engineering and software development. They take these models from the statisticians and program them into software to create high frequency trading algorithms and longer-term trading algorithms. They will also use this data to develop software to manage and view risk quantitatively. Q: Is quantitative analysis the same as technical analysis? A: No. Technical analysts apply a type of qualitative data analysis. While technical analysis attempts to, loosely, base itself on mathematical principles, it is an attempt to qualitatively represent quantitative data. As such, technical analysis is slightly more susceptible to biases. Whereas one TA may view a Fibonacci level as indicating bullish movement, another may view the same level as indicating bearish movement. It is dependent on the TA’s own sentiment and their ability to recognize sentiment and context. Contrast this to a QA, the range that one QA comes up with will likely be very similar to the range of the other QA. That is because QAs all apply the same statistical strategies and tests to identify the data and trends. Biases for QAs are generally counter-intuitive to the process. QAs should not care about what the context or sentiment is, they simply follow the algorithmic processes which are characterized as “If – Then” statements. Q: How does “quant” trading work? A: Traditional quant trading and the quant trading done by hedge funds and banks are accomplished through computers that execute algorithms directly with exchanges. They do not operate through brokerages, they have a direct link to the exchange where they can quickly enter and exit trades that have satisfied the algorithmic conditions. For retail quants like myself, it varies. As I am a quant modeller and not a programmer, I must execute my own trades based on the conditions being met. This introduces the possibility of bias on my part and this bias has gotten me into trouble before! However, other quant traders that are more on the computer programming side, develop their own trading algorithms that will automatically execute their trades, etc. To do this, you need a broker that allows third party integration, in order to integrate your trading platform directly with your developed software. I have no idea how to do this, but I know there are brokers out there that allow this to happen and I know quant retail traders who do, do this. Q: What do I need to be a quant trader? A: Generally, you need a solid understanding of statistics and/or computer programming. In order to effectively develop a working model of a stock, you really need to have a strong understanding of statistics; however, I do know some quants that apply machine learning to their modelling which works okay from what they tell me and can avoid the hassle of developing complex mathematical models of stocks (which takes a long time, I speak from experience!). You also need software and to have a working understanding of a programming language (knowing Excel as a programming language is sufficient!). You need either some form of statistical analysis software or programming software. Software that I frequently see advertised being used at quant firms and banks (at least in Canada) include MATLAB, C++ and Python. I personally use SPSS (in lieu of SAS and MATLAB) and Excel (in lieu of Python/C++). Python is much more powerful generally than Excel and even MATLAB, equally as powerful as SPSS and SAS in its ability to analyze statistical problems and has the ability to actually do more critical appraisals of information than SPSS, SAS, MATLAB and Excel can do. However, for mathematical modelling, I tend to prefer SAS or SPSS combined with Excel but this is mostly because I am a statistician and this software presents the results in a way that I am familiar with (I’m an old dog with no interest in new tricks). A software engineer or programmer would most likely prefer Python. Specifically Anaconda has the same functionality as MATLAB (or so I am told). Q: Is there a cost to the software? A: So, Python is free! So if you know how to use Python or you are interested in learning, you can download it free online! It is open source and very powerful! If your novice, I recommend downloading Python Anaconda, it has everything you need! Excel and SPSS (what I use) tend to be costly. Excel is the cheaper alternative, I think it costs me about 75$ a year (however, I am still a student so I get the student discount, not sure full price). SPSS, MATLAB, SAS are extremely expensive. In excess of over 2,000 USD. There is an option to do an annual licensing agreement for less, but the price would add up. Q: Do I need a degree in mathematics or computer programming? A: NO! You don’t. You can learn this stuff from books and reading. Having a degree doesn’t even guarantee you that you will understand this stuff. I speak to some of my classmates about what I do, and they still don’t understand what I am doing (despite also having MScs in statistics hahaha). It all comes down to your critical application of knowledge! Education is very important, IMO, but its not everything and everyone has the potential to learn if they are truly motivated to! My background was I started as a nurse with a bachelors of science. I fell in love with mathmatics and statistics in my undergrad and ended up pursuing higher education in mathematics, specifically applied statistics. Q: How is quant trading different then technical trading? A: So, as I wrote above, technical trading is the qualitative appraisal of quantitative data. I am not a technical trader and can’t speak too indepth about this process. But I can contrast a little bit, which I will do below! A technical trader may look to see that a particular price point was respected and not surpassed over a number of days. They would likely label this as strong support and would assume that a break of this support would lead to more sustained selling. Contrast to a quant trader, I do not pay attention to any one specific price point. Price action tends to be more on the random side. So I rely on all of the data over many years of trading to develop working ranges and variances between the data. From this, I can determine the range that a stock likes to operate in (whether it be +/- 10 points or, if its TSLA, +/- 30 to 60 points). From here, I can use previous day data to predict a likely range for the next day. When I have that range, I can then express my hypothesis in conditional algebraic forms, like: IF Condition 1 met THEN statement 1 correct AND statement 2 incorrect; or IF Condition 2 met then statement 2 correct and statement 1 incorrect. I then follow linear algebraic principles to identify those conditions and subsequents. For example, for today, SPY opened around 420.28. The range that I calculated for SPY today was 415 to 427. So, the problem that I needed to solve mathematically was: If Condition X met then SPY = 427; OR If Condition Y met then SPY = 415. Then I must use algebra and statistics to determine what Condition X and what Condition Y are. If you read my ideas, you will notice that I express my ideas in linear algebraic form. For my post about SPY today, this is what I had wrote: A break above 424 would indicate bullish sentiment and likely continuation towards 427. A break below 418 would indicate bearish sentiment and likely continuation towards 415.11. If you notice, this can be expressed as a conditional (algebraic) statement: IF X > 424 THEN 427 is met; OR IF X < 418 THEN 415 is met. Now I don’t manually do this because it would be to labour intensive. Which is why I say you need to know a programming language. You can program Python, Excel, MATLAB, C++, etc. to do this for you and identify those ranges. But you need to have the theory in order to understand how to get there and how to give Excel, Python, C++ or MATLAB what it needs to solve the problem for you. Q: Can you recommend books or videos on quant trading? A: So, I have not found any quantitative retail traders on youtube. There are 2 quant developers that actively post on YouTube who have okay content, one being Trading Jesus and the other is Korean Yuppie (who is still kind of novice and hasn’t posted much). Both are from the perspective of quantitative developers; however, this is a completely different skillset from a quantitative modeller. But equally interesting and informative! In terms of books, I would recommend general statistics books and books on programming language like Python or even books on Excel. Excel is generally an under-rated platform that is capable of quite advanced data analysis. Don’t under-estimate it! Excel is involved in my trade planning, execution and profit taking process. It is the thing that dictates what I should do and where I should enter/exit. You also need a solid understanding of the market, how it is organized and how it functions. So general books about market theory and trading are also useful. I have no real recommendations as I haven’t read any books, aside from The Trading Zone, which I found insightful but not helpful. Most of the information you need is available for free online. I wouldn’t invest a huge amount of money in books that are mostly fluff, especially books on day trading. Equally, avoid courses! Don’t buy people’s courses and don’t trust trading “gurus” from YouTube. Hope this answers all of your questions, again please let me know if you have any that I have not addressed! Thank you, take care and as always, trade safe!
AMEX:SPY
by Steversteves
3
$DIDI - The Demise of an IPO (Qualitative Judgement)The current news and development suggest that DIDI will continue to go down. 1. The growth expectation is shattered by delisting the app. 2. The company has a lot of debt, just like any tech companies. 3. Earning growth will be slumpped until they figure out how to get past the hurddles. THE MOST IMPORTANT 4. Management's dishonesty was well documented with the listing process. My current valuation is, This will be a $1 stock soon. With a huge law suit to bear on the backbone, which they will definetely lose. (Clearly documented) The CEO's dishonesty and importance of personal gain rather than the investor's interest (not adding value to the company) is a big big big turn off for me. 2 Things must be noticed or tracked. Let the law suit settle, Unless you see a huge retained earnings that can compensate for the law suit. Want to see the CEO change, or atleast change his attitude and increase his holding by 20% (He just got paid $3,000,000,000 from the IPO). 20% is a conservative number. There are other great options for the money. $POSH will be something I have to evaluate net week. IF YOU TAKE THE POSITION, YOU HAVE TO EVALUATE THE QUALITATIVE NATURE EVERY WEEK
DShort
by UDAY_C_Santhakumar
1
11/8 Weekly Earnings Calendar Spreads (SYY, DHI, CAH, DIS)Description: Some potentially attractive Calendar Spreads I'm looking at putting on based off of the close on Friday. CAH looking especially attractive. Announced Earnings Dates SYY 11/9 DHI 11/9 CAH 11/9 DIS 11/10 Long Call Calendar Spread Levels, break-evens, and R/R will be updated when positions are filled. The boxes on the charts right now are the profit ranges at expiration for ATM Calls You could always spread the puts instead of the calls if you want a slight bearish bias on the stock post earnings. Criteria to enter: At least 4:1 R/R, measured from max profit to debit required to enter. Break-evens outside the expected move Intend to close directly following earnings. *Stops based off underlying stock price, not mark to market loss Only invest what you are willing to lose Break-evens and R/R vary on fill
NYSE:DIS
by BarnardR
RBLX Earnings PlayDescription: Earnings after close today, taking advantage of high IV on same week options and covering with next week's (Calendar Spread). Long Call Calendar Spread Levels on Chart Break-evens 91.34 +16% 69.01, -12% R/R: ~4:1 Positive R/R, stop loss levels built into position. Intend to close before near term expiration. *Stops based off underlying stock price, not mark to market loss The Trade BUY 11/19 79C SELL 11/12 79C Only invest what you are willing to lose Break-evens and R/R vary on fill
NYSE:RBLX
by BarnardR
DXY | DOLLAR INDEX - SHORT Commitment of Traders shows that central banks have been shorting the dollar since June, the slash in 1% slash in interest rates by the FEDs is doing the Dollar any good either. Technically we have a good ol' Cup & Handle, we broke the handle's ascending channel quite impulsively, we are now creating another ascending channel. FUNDAMENTALS MANUFACTURING NUMBERS - MIXED FACTORY ORDERS - GOOD BUT NOT GREAT OIL INVENTORIES - WEAK PMI's - GOOD BUT NOT GREAT EMPLOYMENT - WEAK OVERALL STRENGTH BASED ON NEWS Taking into consideration the market movers, oil and employment I say the fundamentals indicate a weak dollar thus far, tomorrow is a critical day as well. So keep stock of your fundamental releases and assess them for dollar strength or weakness this weekend.
TVC:DXYShort
by Austin_Palmer
BTCUSD Outlook March - April In that Orange Box I expect to see ranging for the rest of the week, then a continuation breakout ending into the 39-40K area the following week. What I would like to see after is price holding the level of 40K for a couple weeks in terms of its daily and weekly closures. The zone at the very bottom is a bonus area for additional spikes we can expect to see. If all goes to plan, We should be seeing healthy recovery PA and a relief around US tax season which would lead us into newer highs in the following months. Thank you sirs and madams! - CB
BITSTAMP:BTCUSD
by CBFXNebula
3