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Showing posts from 2017

Two Strategies you can start trading tomorrow - Time of Day effects in FX continued

My latest post at was on time of day effects in FX and I was claiming that you can actually make money with simple strategies depending on time of day.Below you will find 2 very simple strategies you can play with and make some money. Do not forget sending my 20%, I know I can trust you.I will now test these strategies with M15 bars with my quick and dirty backtester on 15 currency pairs including majors. Backtesting period is 01.01.2007 - 05.05.2017. The charts are profit in pips for the pairs where the strategy has positive P&L, these are not equity curves with equally spaced time, the horizontal axis is number of trades. But obviously equity curves will be similar with some gaps due to days with no trades.I almost did not optimize these strategies, there is room for improvement.Strategy one: Short at GMT 09:15 am, do this on Wednesday, Thursday and Friday, close after 5 hours. Check out EURUSD! In [5]: bt.…

Time of Day effects in FX

Time of day is critical for trading, it is even possible building trading strategies solely depending on time of day (I will keep this for another post)I will be using the concept of quality and define a high quality market, from an intraday timing perspective, as a market when trading range and volume are high and spread is low. I assume this as a good time to trade as trading cost (spread) is low compared to trading opportunity (range and volume)I start importing libraries, In [23]: importmatplotlib.pyplotaspltimportpandasaspdimportbulk_testerasbtimportnumpyasnp%matplotlib inline I then load data for a couple of years for EURUSD, here I am using my own library for loading it from a csv file containing 15 minute bars and sampling at each 60 minutes. In [24]: pair="EURUSD"dftest=bt.get_data(pair,'BT',resample_period='60T') Then I add three columns; hour for doing the hourly grouping, spread and range In [25]: dftest['hour']=dftest.index.hourdftest['sprea…