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5

With respect to what you need, you have to consider different aspects of optimal trading: the Almgren-Chriss framework (cited by Anna, since Jim and Alex -amongst others- extended it) focus on obtaining an optimal trading rate, it is nice but not really what you need. You can nevertheless use it to plan / schedule your trading during the day. but what you ...


4

The premise of your question is wrong. European bond markets usually quote clean prices (without the accrued) for performing bonds - exactly like U.S., Canadian, and most Latin American bond markets. Both in American and in European bond markets, bonds begin to be quoted dirty (with the accrued, total proceeds) only when they are on the verge of defaulting. ...


4

To supplement @Dimitri's excellent answer, I recommend a little booklet called "Government Bond Outlines," published by JPMorgan's index team. This is easily obtainable from JPMorgan's research website. It lists, for each government bond market, the market characteristics, calculation convention, and trading basis (e.g., quotation, tick size, ...


4

There is no authoritative source. If you're dealing with vast quantities of diverse bond quotes, then it's very hard to interpet them correctly all the time, although you might get be right most of the time with less effort. As general guidelines, yes, IG is usually yield, and HY is usually price. But some issuers (e.g. EM eurobonds) are usually price even ...


4

Almost always, the market convention is to use for yield the same frequency as the coupon payment frequency. However in a few markets, the market convention is to convert this yield to the frequency of the local government bond. For example, if the local government bonds usually pay annually, as they do in Eurozone, and some corporate bond pays quarterly or ...


4

In the end I found that fitting a SABR smile to each tenor (borrowing a result from this answer) was sufficient to build a local vol surface that was smooth and well-behaved enough to build a variance surface worked nicely. I also fitted a Heston model to it, and the two surfaces do look fairly similar. Here is the final code and the fits generated (the long ...


3

I tried something along these lines in Quantlib python a few weeks ago. Slightly more simple compared to your approach I think: start with a standard delta quote convention for FX vols (10D puts, 25D puts,ATM,25D call, 10D call) calculate the moneyness of the options to obtain the strike set (this will be a large strike set since each option maturity will ...


3

+1 for "feeling like the data is out there to be parsed for free". lol If data is just for toys, do: http://www.dxfeed.com/historical-tick-data/ They offer (free) tick data for May 6 2010 (flash crash). Scrape google. This question: Free intra-day equity data source


3

I reckon it's the $\frac{log(ASK)+log(BID)}{2}$, just simple arithmetic average as it makes sense, also considering logarithmic returns, when you can only take a differences from log prices. Also, the second alternative would yield negative 'prices' for spreads lower than 1, which cannot serve as a 'price' indicator.


3

The relationship between S and F is known as "the basis". You can theorize a relationship of the form $$F=S \exp(c(T-t))$$ or the simpler, approximate $F = S + C (T-t)$. Knowing $F,S$ and $T-t$ (the time to maturity) you can estimate $c$ or $C$ which we might call the "basis per day to maturity". If you constantly estimate it and plot it, you will see ...


1

I will try to be as concise as possible. For obvious reasons, if you do not have any trades, choose the quotes, because they reflect the intention of a player to trade at that level of price/implied_vol at a certain point in time (where we have no trades because those quotes are not matched by other traders). If instead you have a quote and a trade ...


1

It's a little dependent on whether its listed or otc options but your question about implied volatilities probably addresses the issue the best. I would calculate the implied volatility from the real transactions noting whether its a buy or sell and then do the same for the markets that you are seeing and compare them depending on what the market has done ...


1

The reason the spreads were off is that the data came from MARKIT, and MARKIT often includes a 3M spread (but does not always publish it). So the 3M Quoted Spread and 3M Par Spread are exactly the same (but unfortunately invisible). And therefore Par and Quoted for >3M will not be exactly the same.


1

I don't recommend linear interpolation of DFs and the swap rates you are applying this to are either against 12M libor which is illiquid or you are not accounting for Quarterly or Semi-Annual floating sides. And what I'm going to suggest uses a single curve framework which is long outdated. But that being said and given the nature of what's been asked... ...


1

Technical analysis is not quite in my wheelhouse, but it's been an interesting topic to me, so hopefully I can lend a hand. Let's start with some basic assumptions: Because OBV is based on volume, there is obviously a huge range as you've pointed out. This makes comparison straight across companies impossible. To compare companies, you need to take out ...


1

You can use Thomson Reuters Tick History to get intraday data at 1 minute interval. However, you need to pay for that. Many university provide access to this database.


1

I made the next C++ conversion function. int Convert( Bar *bars, const Tick *tick, int tickCount, int lookback ) { #define Normalize(a) double((int((a)*m_scale+0.5))*m_point) unsigned int m_end = 0; int count = 0, i = 0; Bar *pBars = bars[count++]; // lookback - bar's timeframe in seconds const Tick &t = tick[i++]; pBars-&...


1

Pick a time range of traded prices, open is the first value in the range, high is the max of the range, low is the min and close is last value in the range.


1

Bars represent an summarized view of what happened during a given period of time of a single given value. Therefore, you need to first pick the value you want to summarize: Bid, Ask or Last Trade and use values of only these measures to build your bars.


1

Your confusion is certainly coming from a distinction between Price and Yield. 1 - You're definitely right in regards to Bond Price as 99 1/8 = 99.125. Likewise 99 1/32 = 99.0313 (assuming 100 PAR). It's worth highlighting on the fact that this convention is only applicable to US bond prices, as far as I am concerned. 2 - By contrast, Bond Yield is the ...


1

Supply and demand... If you want an event that produce a change in the value of a currency, just look at the ruble. As Russia, gets more and more isolated and inflation spins out of control the ruble lose its value against other currencies.


1

R is very useful for downloading data from Yahoo/Google . Here is an example for downloading from Google Finance : library(quantmod) getSymbols("DRRX;AAPL;AMZN", from="2014-01-01", to="2014-11-20", src='google') Just adjust the from and to dates as needed. This will download the OHLCV data from google finance to your R global environment.


1

They are both just partial reflections (not including the order book) of the real process that happens in exchange. If you want to answer the question yourself, it's essential to learn how Exchange's Matching Engines work. The real underlying information is what enters into the matching engine (what traders send to it). For the sake of simplicity, there are ...


1

These are two separate and distinct pieces of data. The relative "advantage" or "disadvantage" of one over another is entirely up to you and your model, not some rule of thumb. Each data set provides "one half", if you will, of the view of the market. Quotes tell you what passive participants are willing to do. They are, in effect, an indication of interest ...


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