| Value | Meaning |
|---|---|
| DB-statistic << 2 | positive serial correlation |
| DB-statistic ~ 2 | no first-order correlation |
| DB-statistic >> 2 | negative serial correlation |
Gerard Martinez Rosell GerardBCN
GerardBCN
/ manipulation_explorer.ipynb
Created
July 16, 2018 12:48
Statistical analysis of Bitcoin price manipulation by Tether issuance in late 2017
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GerardBCN
/ policy_iterator_RL_gridworld.ipynb
Created
December 20, 2018 13:59
Policy iterator for RL applied to gridworld
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GerardBCN
/ monte_carlo.ipynb
Last active
July 31, 2020 01:58
State-value function approximation for the gridworld task using Monte Carlo simulations
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GerardBCN
/ gist:9e0357c70873a2cfe5788d610b2d8261
Created
December 24, 2018 19:13
Using temporal-difference - TD(0) or one-step TD to solve the gridworld task
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GerardBCN
/ stock_price_autoencoding.ipynb
Created
January 18, 2019 21:15
Stock market Bitcoin data compression with autoencoders
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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 |
GerardBCN
/ returns.py
Created
April 23, 2019 15:29
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| def returns(candles_close_prices): | |
| return np.diff(np.log(candles_close_prices)) |
GerardBCN
/ Durbin-watson.md
Last active
April 23, 2019 15:46
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| import numpy as np | |
| # expects a numpy array with trades | |
| # each trade is composed of: [time, price, quantity] | |
| def generate_dollarbars(trades, frequency=1000): | |
| times = trades[:,0] | |
| prices = trades[:,1] | |
| volumes = trades[:,2] | |
| ans = np.zeros(shape=(len(prices), 6)) | |
| candle_counter = 0 |
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| import numpy as np | |
| # expects a numpy array with trades | |
| # each trade is composed of: [time, price, quantity] | |
| def generate_volumebars(trades, frequency=10): | |
| times = trades[:,0] | |
| prices = trades[:,1] | |
| volumes = trades[:,2] | |
| ans = np.zeros(shape=(len(prices), 6)) | |
| candle_counter = 0 |
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