FOREX - Trends, forecasts and implications 2015 - page 600
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pips targets 3-5 pips stop 15, and scalping targets 15-30, stop 100 pips... Scalping with indicators, and pipsing by intuition of the heel of the left hind leg...
All right, stop being silly.)
Understand a simple thing, trading cannot be based on sticks or dashes drawn on a chart out of the blue.
Closed it all up and this is what I got.
I'm gonna put some fleas in my pocket. (I'll get some money out.)
Closed everything down and this is what I got
I'm gonna put some fleas in my pocket. (I'll take out some money)
Fleas jumped in my pocket.
Mythical you're asleep and the audi is here to collect debts )
Fleas have jumped into karma.
Bought EUR/USD and GBP/USD
Forum on trading, automated trading systems and trading strategy testing
Predicting the market based on macroeconomic indicators
gpwr, 2015.02.12 05:15
So, the task is to predict the S&P 500 index based on available economic indicators.
Step 1: Find the indicators. The indicators are publicly available here: http://research.stlouisfed.org/fred2/ There are 240,000 of them. The most important one is GDP growth. This indicator is calculated every quarter. Hence our step is 3 months. All indicators on shorter timeframe are recalculated to 3 months, the rest (annual) are discarded. We also discard the indicators for all countries except USA and the indicators which do not have a deep history (at least 15 years). So we laboriously sift out a bunch of indicators, and get about 10 thousand indicators. Let's formulate a more specific task - to forecast S&P 500 index one or two quarters ahead, having 10 thousand economic indicators with a quarterly period. I do everything in Matlab, but it is also possible to do it in R.
Step 2: Convert all the data to a stationary form by differentiating and normalizing. There are a lot of methods. The main thing is that the transformed data can be recovered from the original data. No model will work without stationarity.
Step 3: Choose a model. This could be a neural network. Optionally a multi-variablelinear regression. Optionally a multivariable polynomial regression. Having tried linear and non-linear models, we conclude that the data is so noisy that it does not make sense to fit a non-linear model as y(x) graph where y = S&P 500 and x = one of 10 thousand indicators, is almost a round cloud. Thus, our task is even more clearly formulated: to predict the S&P 500 index for one or two quarters ahead, having 10 thousand economic indicators with a quarterly period, using multivariable linear regression.
Step 4: Choose the most important economic indicators out of 10 thousand (reduce the dimension of the problem). This is the most important and difficult step. Let us assume that we take the history of the S&P 500 which is 30 years long (120 quarters). In order to represent the S&P 500 as a linear combination of various economic indicators, it is sufficient to have 120 indicators to accurately describe the S&P 500 during these 30 years. Moreover, the indicators can be absolutely any kind of indicators, in order to create such an accurate model of 120 indicators and 120 values of S&P 500. Thus, we shall reduce the number of inputs below the number of described function values. For example, we are looking for 10-20 of the most important indicator-entries.