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Itâs time for the 5th and final part of the Build Better Strategies series. In part 3 weâve discussed the development process of a model-based system, and consequently weâll conclude the series with developing a data-mining system. The principles of data mining and machine learning have been the topic of part 4. For our short-term trading example weâll use a deep learning algorithm, a stacked auto
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An experienced trader knows what behavior to expect from the market based on a set of indicators and their interpretation. The latter is often done based on his memory or some kind of model. Finding a good set of indicators and processing their information poses a big challenge. First, one needs to understand what  factors are correlated to future prices. Data that does not have any predictive qua
About This is a Python wrapper for TA-LIB based on Cython instead of SWIG. From the homepage: TA-Lib is widely used by trading software developers requiring to perform technical analysis of financial market data. Includes 150+ indicators such as ADX, MACD, RSI, Stochastic, Bollinger Bands, etc. Candlestick pattern recognition Open-source API for C/C++, Java, Perl, Python and 100% Managed .NET The
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Python Algorithmic Trading Library PyAlgoTrade is a Python Algorithmic Trading Library with focus on backtesting and support for paper-trading and live-trading. Letâs say you have an idea for a trading strategy and youâd like to evaluate it with historical data and see how it behaves. PyAlgoTrade allows you to do so with minimal effort. Quickstart (deprecated) Basana Quickstart Main features Fully
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