Using Fibonacci Extensions to set profit targets?
fibonacci levels (retracements,expansions, arcs) are all arbitrary numbers with no statistical significance. that said thousands of traders world over use, view and depend on fib numbers in their trading ranging from forex, stock commodities etc the point is if it's traded a fibonacci number has been used on it, because of this unanimity on their significance & application the fibonacci's thus act as valid anchors since so many traders are looking at the same levels (self-fulfilling prophecy). the values of the fib numbers are all equally significant i.e the 23.6. 38.2, 50, or 61.8 are statistically all equally likely to occur. you just have to be vigilant as your trade approaches the fib levels. I have never seen a backtest showing that prices tended to be attracted by / to revert around Fibonacci levels. The fact that many people use them doesn't mean that they can be turned into a profitable system... I have on the other hand seen many backtests showing that they don't do anything, such as the one described in this article: At least in this sample of market data, using this particularly specification for swings, we find no evidence that Fibonacci ratios are significant in the market.   Perhaps I have missed something significant, or perhaps I am merely completely wrong in my analysis, but one thing should be clear—the burden of proof should lie on the people offering arcane and complex methodologies, when simpler methods work just as well or better in the marketplace. If Fibonacci ratios are the key to the markets, where are the quantitative tests? Where’s the proof?
You understand that inhalation/smoking isn't the only route of administration for THC (active drug in pot), right? Pseudo-Pharmaceutical grade cannabinoid extractions are already used as medication for those with excruciating GI issues, glaucoma and seizure disorders.   The only reason smoking the buds is the preferred method of consumption is because it's the base form and easy to come by. It's (typically) too expensive for one to refine the drug themselves, too risky legally or too dangerous without a controlled laboratory setting. Due to the length of its illegality, extensive research is prohibited to even determine what benefits it could even have in these forms.   You have no idea what therapeutic dosing or high grade cannabinoids could even do because no one is allowed to do it. Especially not doctors trying to keep their medical licenses.   Also I disagree that the benefits of opioids outweigh the side effects. That's why we're having the opioid crisis in the first place. Highly addictive and can lead to overdose. While there is no single case of THC overdose.   (Forensic drug chemist here, not talking out my ass) If the building has no income, it also probably has minimal expenses. The heat, water and electricity costs are nearly zero. They are letting the value depreciate, and taking it off the taxes. I also suspect the condition of the building is poor, so any effort to make the building productive would be very costly. Many cities combat this by setting the tax on empty buildings or empty lots at a much higher rate. Or they set the value of the property at a high valuation based on what it could generate. Sometimes this is only targeted at some sections of the city to encourage development. They also offer tax breaks when the owner of a house has the house as their principal residence. "As a rule, if you will, recruiting is broken down into ""targets"", ""semi-targets"", and ""non-targets"".  A target school is one where almost all bulge bracket investment banks recruit heavily, semi-targets are where some recruit, and non-targets are where almost none recruit. This isn't an end-all-be-all, because many middle market and boutique banks recruit at a wide variety of schools, but use this as a guide essentially.  **Targets**: Harvard, Wharton, Princeton, MIT, Duke, NYU, Stanford, Columbia, Yale, Brown, Dartmouth, Cornell, Chicago, UC-Berkeley, and other top schools I might've left out (Amherst or other good liberal arts schools, I'm less familiar here)  **Semi-Targets**: Vanderbilt, UNC Chapel Hill, Duke, Notre Dame, Emory, Michigan, Indiana, Texas, etc. (think great state schools)  **Non-Targets**: You get the picture. Less recruiting here, but some might still get recruited at.   As far as programs go, Economics, Finance, Engineering, Physics-it could be a million things. What matters is that the degree is at least somewhat challenging and you show an interest in finance." "RBL is likely *reserve based lending*, which is a type of asset-based financing in oil&amp;gas world where debt is secured by oil reserves.  Presumably context is that have an oil&amp;gas company that the analyst thinks should be acquiring other companies.  To fund that, the analyst is saying the parent company should borrow money against their reserves (should be relatively inexpensive capital b/c asset-backed) and then using it to fund acquisitions.  For the target's capital structure, those amounts are equity infused by the Parent (even though the Parent got the money by borrowing it).  The Target can then borrow money against its own operations... this borrowed money will ""gear"" up the Target -- gearing is just a term for leverage, typically specifically debt-to-equity ratio.  So to fund the acquisition, the Parent is (1) going to borrow using a RBL on Parent's assets to come up with equity to put into target and (2) going to borrow against Target's business.  Presumably today Target is relatively debt free or even cash heavy." "Well it depends. I doubt the professor is looking for the ""basic"" answer as you described it. He's mostly likely looking for the above answers (i.e. the Rf is used to value a call in the B-S model).   Like in all classes, you need to know what the prof is really looking for, because many questions can be interpreted many different ways and unfortunately most teachers aren't clear in asking what they want." "@farnsy has provided a good answer. I'm only addressing my comment about the data quality. The portfolio optimization technique you employed is very sensitive to the inputs. In particular, it relies entirely on the mean and (co)variance assumptions (i.e. the first two moments) and the results could change drastically with very small amount of change in the inputs. To see that, you can make up some inputs for the solver you have, and try adjusting the inputs a little bit and see the results. Therefore if you decide to take this approach, data quality is very crucial. EDIT: What I meant by ""data quality"" I have no experience with this website but this should be easy to spot check. The answer is usually ""yes"" for liquid assets. Illiquid assets can often be priced at a level with no volume, and the bid-ask spread could be huge.  Should I close my eyes on the fact that these cryptocurencies aren't perfectly priced in my currency and use another one (such as the dollar) You seem to have concern about data quality in at least the price quoted in your currency and are thinking about using data quoted in USD, but would it be any better? The law of one price tells us that there shouldn't be any discrepancy between prices in different currencies (otherwise there would be arbitrage). In addition, (when compared to traditional assets) cryptocurrency price data has a shorter data history, and with lower liquidity in the market. The short history means you have less data to infer the characteristics of the price behavior. Low liquidity means the volatility may well be underestimated. So we have an input-sensitive technique combined with not-so-perfect data. I wouldn't allocate my money solely based on the result of this exercise. EDIT: I have quite some reservation about doing portfolio optimization for cryptocurrency. Personally I'm not a fan of the technique as is. The optimization has an underlying assumption that returns follow a certain distribution, and correlation is fixed. I don't know if you can make such assumption for cryptocurrencies. From what I read about BTC for example, it seems to have a high risk exposure concerning Chinese monetary policy. For that kind of assets perhaps a fundamental analysis approach is a better one.  Also if you would like to learn more about portfolio optimization, try quant.SE" I implemented this in MatLab about 10 years back. You just calculate your conditional variance of the required assets (x_i), use matrix multiplication on the correlation matrix (rho_i_j) from the same asset (this could be a point of research but unless you are using extreme conditions on the VaR it makes little difference) then apply a standard Markiowitz optimisation approach. You can then just use simple Sharpe ratio (marginal return over conditional risk) at every point on the efficient frontier. Then choose the maximum Sharpe ratio point. Yes, exactly. VaR is just a single tailed confidence interval.  To go from model to strategy, you need to design some kind of indicator (i.e. when to buy and when to short or stay out). In practice, this will look like a large matrix with values ranging from -1 to 1 (corresponding to shorting and holding respectively) for each security and each day (or hour, or minute, or tick, etc.), which you then just multiply with the matrix of the stock returns. The resulting matrix will be your daily returns for each stock, you can then just row sum for daily returns of a portfolio, or calculate a cumulative product for cumulative returns.  A simple example of an indicator would be something like a value of 1 when the price of the stock is below the 30 day moving average, and 0 otherwise. You can use a battery of econometric models to design these indicators, but the rest of the strategy design is essentially the same, and it's *relatively* easy to build a one-size-fits-all back-testing code.  I'll try to edit this post later and link a blog that goes through some of the code.  Edit: [Here](http://www.signalplot.com/simple-machine-learning-model-trade-spy/) is a post that discusses implementing a simple ML strategy. You can ignore most of the content but if you go through the github, you'll see how the ML model is implemented as a strategy.  An even easier example can be found from [the github connected to this post](http://www.signalplot.com/how-to-measure-the-performance-of-a-trading-strategy/), where the author is just using a totally arbitrary signal. As you can see, deriving a signal can be a ton of work, but once you have, actually simulating the strategy can be done in just a few lines of code.  Hopefully the author won't mind me linking his page here, but I find his coding style to be very clean and good for educational purposes.