Norgate Data
Norgate Data, based in Australia, was founded in 1992. Norgate provides end-of-day price data for stock markets in Australia, Canada and USA, worldwide futures price data, cash commodifty and foreign currency data. Such data can be charted through Technical Analysis charting packages such as MetaStock and AmiBroker, and accessed in programming languages such as Python.
Norgate's data services are utilised by retail-level non-professional traders and investors, Government entities such as the Brazil Government, [1] large corporate entities such as Deutsche Börse,[2] analysts[3][4] and University researchers.[5][6][7][8][9][10][11]
References
[edit]- ^ United States Subsidies on Upload Cotton - Opening Statement of Brazil to the World Trade Organisation, Brazil Ministério das Relações Exteriores, February 2007
- ^ CX Commodity Indices, Deutsche Börse, 2006
- ^ Hill CMT, Arthur (2018-12-30). "Finding Consistent Trends with Strong Momentum - Rsi for Trend-Following and Momentum Strategies". Rochester, NY. SSRN 3412429.
{{cite journal}}: Cite journal requires|journal=(help) - ^ "Market Timing Charts". Bollinger Bands. Retrieved 2019-10-29.
- ^ Do Wall Street Fundamentals work in the ASX 200?, Vanstone & Agrawal, Bond University, 2006
- ^ Customer Order FLow and Exchange Rate Movement - Is There Really Information Content?, Marsh & Rourke, Faculty of Finance, Cass Business School, City University, London, 2004
- ^ Order Flow and Central Bank Intervention - An Empirical Analysis of Recent Bank of Japan Actions in the Foreign Exchange Market, Ian W Marsh, Faculty of Finance, Cass Business School, City University, London, 2006
- ^ Combining Technical Analysis and Neural Networks in the Australian Stockmarket, Vanstone & Finnie, Bond University, 2006
- ^ Applied Financial Risk Management for the Shipping Industry Using IMAREX Derivatives, Runar A. Skjetnem, Norwegian School of Economics and Business Administration, 2006
- ^ The Buy-Write Strategy, Index Investment and the Efficient Market Hypothesis, O'Connell & O'Grady, presented to European Financial Management Association Annual Meeting, 2007
- ^ Lee, Jinho; Kang, Jaewoo; Gherghina, Stefan Cristian (10 April 2020). "Effectively training neural networks for stock index prediction: Predicting the S&P 500 index without using its index data". PLOS ONE. 15 (4): e0230635. Bibcode:2020PLoSO..1530635L. doi:10.1371/journal.pone.0230635. PMC 7147736. PMID 32275721.