Mindblown: a blog about philosophy.
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High frequency, low latency – where Python fails
Our overview would be incomplete without mentioning HFT. Its roots are in the financial crisis of 2008 when liquidity became the main issue in most, if not all, developed markets. Exchanges started to offer an incentive to those who provided liquidity, waiving many restrictions that previously required liquidity providers to be regulated. As a result, many…
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Market-making – profiting on liquidity provision and associated risks
We already considered market-making in detail in the Market makers – comfortable, sophisticated, expensive section in Lesson 3, FX Market Overview from a Developer’s Standpoint, so there’s no need to repeat ourselves here. We will mention market-making here only for consistency as an example of a sell-side trading strategy. If we want to classify market-making as pertaining to alpha- or beta-generating strategies,…
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Event-driven trading strategies
An event-driven strategy mostly relies on non-market data such as economic or political news. We already considered the impact of these events on the market price (see Lesson 6, Basics of Fundamental Analysis and Its Possible Use in FX Trading). So, an event-driven strategy can attempt to enter when significant news hits the market and exit soon after. The problem with…
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Statistical arbitrage
As we saw in the previous section, arbitrage is based on the idea of mispricing: a situation in which an asset is priced incorrectly. But to say whether something is priced incorrectly or correctly, we need a reference that is known to be priced correctly, don’t we? In classical arbitrage, such a reference is the asset price…
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Arbitrage – let’s earn from others’ mistakes
Arbitrage in financial markets means taking advantage of situations when the same asset is priced differently at different trading venues. Such a situation is usually called mispricing (there are other meanings of this term, and we will get back to it in the very next section about statistical arbitrage). Due to the colossal fragmentation of the FX market (see Lesson 3, FX…
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Alpha classics – trend-following, mean reversion, breakout, and momentum
Let’s quickly recap the idea of generating alpha: we want to beat the market or perform better than an index (or just the rate itself in FX trading) by actively managing the position in the market. This means we try to buy when we expect the price to go up and we try to sell when…
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Options – stable income with unlimited risk
This subheading sounds ridiculous, doesn’t it? How can stable income go hand in hand with unlimited risk? To understand it, let’s first understand what an option is and how it’s possible to trade them. An option is a derivative (see Lesson 1, Developing Trading Strategies – Why They Are Different, for a brief explanation of the underlying and derivatives) that gives its holder…
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Bank indices – more details, more confusion
These indices sometimes only serve indicative purposes; others are tradable instruments. For example, Deutsche Bank lists plenty of FX indices. Some of them represent classical buy and hold while others track Deutsche Bank’s own active investment. So, if you develop an alpha-generating strategy, then it makes more sense to compare its performance against one of these indices. Let’s…
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Common FX benchmarks – the USDX
The USDX (ticker DXY) is “a live measure of the performance of the US dollar against a basket of other currencies”. If we are to interpret it in terms of investment, then it is quite similar to stock investment, but instead of stocks, you buy or sell the US dollar versus a basket of currencies (which at the…
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Using currency rates as a benchmark
We can use exactly the same approach as with a stock index: compare the return of the trading strategy to the return in the currency itself for the same given period of time. For example, if we look at the historical rates of EURUSD from January 2021 to January 2022, we can see that they declined…
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