Lecture 1: Introduction to Research — [📝Lecture Notebooks] [
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[
a8lo
/ list.md
Created
November 21, 2024 14:38
— forked from ih2502mk/list.md
Quantopian Lectures Saved
a8lo
/ options.adoc
Created
November 21, 2024 14:40
— forked from johnymontana/options.adoc
My entry for the Neo4j GraphGist December competition. Very simplified options trading in a graph.
This GraphGist will begin to explore how stock option data can be modeled as a graph, some simple Cypher queries for calculating payout at expiration for an options contract and a very basic look at finding profitable options trades given a specific forecast. Please note that some of the concepts here have been simplified and are only meant as an educational overview of exploring Neo4j and graph data modeling.
a8lo
/ tickbar_generator.py
Created
November 21, 2024 14:40
— forked from GerardBCN/tickbar_generator.py
Tickbar generator
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| # expects a numpy array with trades | |
| # each trade is composed of: [time, price, quantity] | |
| def generate_tickbars(ticks, frequency=1000): | |
| times = ticks[:,0] | |
| prices = ticks[:,1] | |
| volumes = ticks[:,2] | |
| res = np.zeros(shape=(len(range(frequency, len(prices), frequency)), 6)) | |
| it = 0 | |
| for i in range(frequency, len(prices), frequency): | |
| res[it][0] = times[i-1] # time |