Forex Trend Indicator on Binary trading
Monday, 23 January 2017
Friday, 20 January 2017
Better Candles Indicator
development
January 20, 2017
This simple indicator provides additional options for customizing the look of your candlestick charts on MarketScope.
It supports separate outline colour, fill colour, hollow/solid fill for up and down candles. Line thickness and candle body width can also be configured. Set it up how you like it, and add it to you default chart template for all new charts. It can also be used for multi-time-frame charts, e.g. showing D1 candles super-imposed on lower time-frame chart.
Read more ...
It supports separate outline colour, fill colour, hollow/solid fill for up and down candles. Line thickness and candle body width can also be configured. Set it up how you like it, and add it to you default chart template for all new charts. It can also be used for multi-time-frame charts, e.g. showing D1 candles super-imposed on lower time-frame chart.
It is available on the store.
Tuesday, 17 January 2017
GBP/USD prior to UK PM's big speech on BREXIT
news
January 17, 2017
Candle Profile showing buying/selling ahead of UK Prime Minister Theresa May's long awaited speech on BREXIT.
This view of the Candle Profile indicator uses the "real volume" (buying/selling volume) from FXCM to generate the profile (green = buying, red = selling, yellow = delta). You can see the selling in these last few candles, but price still went up. This is the big boys pushing the price higher to close the gap.
I expect it to go both directions during the news event (Theresa May will be speaking at 11.45am UK time).
Read more ...
This view of the Candle Profile indicator uses the "real volume" (buying/selling volume) from FXCM to generate the profile (green = buying, red = selling, yellow = delta). You can see the selling in these last few candles, but price still went up. This is the big boys pushing the price higher to close the gap.
I expect it to go both directions during the news event (Theresa May will be speaking at 11.45am UK time).
Sunday, 15 January 2017
Wednesday, 11 January 2017
USD/JPY Candle Profile showing buying/selling during "news event"
USD/JPY
January 11, 2017
Lot's of movement today on the USD/JPY before, during and after President-elect Trump's news conference.
Some great buying & selling showing up on the Candle Profile.
The Candle Profile really works well for this type of scenario. More information on the Candle Profile here.
Read more ...
Some great buying & selling showing up on the Candle Profile.
The Candle Profile really works well for this type of scenario. More information on the Candle Profile here.
Machine Learning for Trading (Part 4) - Decision Trees
Machine Learning
January 11, 2017
Decision tree learning uses a decision tree as a predictive model which maps observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves).
It is one of the predictive modelling approaches used in statistics, data mining and machine learning.
Decision trees where the target variable can take a finite set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees.
Tools are available (for example scikit-learn Python library) to enable efficient generation of decision trees.
The trees are generally binary trees, i.e. each node has 2 branches. A "decision" at each node causes the search to proceed down either the left or right "branch", and so onto the next node, etc. until a "leaf" node is reached.
The decisions are basically tests about the data set. For example, suppose we collected data from various technical indicators: RSI, STOCHASTICS, MVA, etc. A tree might be constructed something like the diagram below...
The above example is a classification tree, i.e. the leaf nodes are things like buy/sell/flat. It's also possible to construct trees that result in a numerical value, e.g. the leaf nodes could be some numeric value, e.g. prediction of price move. These are sometimes called regression trees.
By the way, the above tree is just for illustration purposes.
The tree doesn't have to be balanced (i.e. no need to have equal number of nodes on both sub-branches), criteria can appear multiple times, not all criteria need to be used, etc. It's actually a very flexible approach, since the data can be very heterogeneous (numerical values, classes, yes/no, etc.)
The most efficient way to develop the tree will select criteria that provide the most information at each step. For example, suppose we were using a decision tree to guess a playing card; the first question should be "is the card red?" as this instantly splits the data in half.
Decision trees are prone to over-fitting, but this can be mitigated by using ensemble methods (e.g. several different trees and using majority vote).
The process of generating a decision tree is also potentially useful in itself. For example, we can throw a heap of technical indicator data at the tree generation process, and see which are the most important (i.e. the ones performed earlier). This information may be useful for further studies.
Read more ...
It is one of the predictive modelling approaches used in statistics, data mining and machine learning.
Decision trees where the target variable can take a finite set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees.
Tools are available (for example scikit-learn Python library) to enable efficient generation of decision trees.
The trees are generally binary trees, i.e. each node has 2 branches. A "decision" at each node causes the search to proceed down either the left or right "branch", and so onto the next node, etc. until a "leaf" node is reached.
The decisions are basically tests about the data set. For example, suppose we collected data from various technical indicators: RSI, STOCHASTICS, MVA, etc. A tree might be constructed something like the diagram below...
The above example is a classification tree, i.e. the leaf nodes are things like buy/sell/flat. It's also possible to construct trees that result in a numerical value, e.g. the leaf nodes could be some numeric value, e.g. prediction of price move. These are sometimes called regression trees.
By the way, the above tree is just for illustration purposes.
The tree doesn't have to be balanced (i.e. no need to have equal number of nodes on both sub-branches), criteria can appear multiple times, not all criteria need to be used, etc. It's actually a very flexible approach, since the data can be very heterogeneous (numerical values, classes, yes/no, etc.)
The most efficient way to develop the tree will select criteria that provide the most information at each step. For example, suppose we were using a decision tree to guess a playing card; the first question should be "is the card red?" as this instantly splits the data in half.
Decision trees are prone to over-fitting, but this can be mitigated by using ensemble methods (e.g. several different trees and using majority vote).
The process of generating a decision tree is also potentially useful in itself. For example, we can throw a heap of technical indicator data at the tree generation process, and see which are the most important (i.e. the ones performed earlier). This information may be useful for further studies.
Tuesday, 10 January 2017
Machine Learning for Trading (Part 3) - k-Nearest Neighbours
Machine Learning
January 10, 2017
A popular and simple machine learning algorithm is k-Nearest Neighbours (or k-NN for short).
The k-NN algorithm is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression:
We can use the k-NN algorithm to find the k nearest neighbours in our training data to this target vector. In this sense, it is essentially operating as a pattern matching algorithm. The output could be something like the average price-move of our k nearest matches, or even a buy/sell classification.
Read more ...
The k-NN algorithm is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression:
- In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbours, with the object being assigned to the class most common among its k nearest neighbours (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbour.
- In k-NN regression, the output is the property value for the object. This value is the average of the values of its k nearest neighbours.
For example, suppose we have training data for some fruit with measurements of the size, colour, acidity and a label stating the type of fruit (orange, lemon, lime). We can use k-NN to "predict" the class of an unknown fruit based on its measurements of size, colour and acidity. Alternatively, we could "predict" the value of acidity based on the fruit's colour, size and type. The former case is classification, the latter is regression.
For time-series analysis, this is generally just treated as a regression in 1-dimension, i.e. predicting a value of price from the"k" nearest time samples. This works very well for smoothing the data, but unfortunately breaks down somewhat at the right-hand edge, since the k nearest samples will always be to the left of it. In fact, the result is somewhat similar to a simple moving average.
The following graph shows some sample time series data, with a k-NN (k = 3) and a simple moving average (length = 3) for comparison.
The 1 dimensional k-NN does a great job of smoothing the data, but is not particularly good for prediction of the future (or at least no better than a moving average).
Another approach is to use a multi-dimensional k-NN. For example, suppose we use a feature vector "v"of 10 dimensions (denoted as v[0] to v[9]). Now, let v[0] be the price at time t0, v[1] be the price at time t1, v[2] be the price at time t2, etc.
We can use the k-NN algorithm to find the k nearest neighbours in our training data to this target vector. In this sense, it is essentially operating as a pattern matching algorithm. The output could be something like the average price-move of our k nearest matches, or even a buy/sell classification.
However, there are a number of issues:
- We need some kind of price invariance built into the system. For example, a particular pattern based around 1.100 should be matched with an identical pattern that happens to be based around 1.200 or 1.300, etc.
- Very large data sets are needed, and unlike the 1-dimensional case where we only had to search the last k points, we have to search the whole training set for our matches. This presents challenges for storage and performance.
One way to achieve the price invariance can be built in by looking at % change. For example, simply re-stating the feature vector so that v[0] = 0, and v[1] = (price at time t1 - price at time t0) / (price at time t0), etc. Similarly, one could use "returns" or "log returns".
Tackling the large amount of data requires a number of innovations:
- Reducing which data is used. For example, suppose we only care about price patterns that occur when the price is "over-bought" or "over-sold" then there is no need to store data for all the other cases.
- Using data structures like k-d trees to store the data more efficiently can drastically reduce the search time.
Monday, 9 January 2017
New Release: Candle Pattern Indicator and Scanner (now with alert notifications)
Candle Pattern
January 09, 2017
I've added alert notifications to the Candle Pattern Indicator and Candle Pattern Scanner.
When alerts are enabled, the indicator will cause an alert (pop-up alert and/or sound) whenever a new candle pattern is detected.
More information can be found here and here.
Read more ...
When alerts are enabled, the indicator will cause an alert (pop-up alert and/or sound) whenever a new candle pattern is detected.
More information can be found here and here.
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