samsonq
/ hedge-fund-rankings.txt
Created
July 28, 2023 21:17
— forked from gg7/hedge-fund-rankings.txt
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| Ranked by discretionary assets managed in hedge funds worldwide, in millions, as of June 30, 2018, unless otherwise noted. | |
| Rank Manager Assets Change from 2017 | |
| 1 Bridgewater Associates $132,756 7.9% | |
| 2 AQR Capital Mgmt. $83,700 9.2% | |
| 3 Man Group $59,100 11.3% | |
| 4 Renaissance Technologies $57,000 17.3% | |
| 5 Two Sigma Inv./Two Sigma Advisers $38,800 9.6% | |
| 6 Millennium Mgmt. $35,314 2.7% | |
| 7 Elliott Management $35,000 7.0% |
samsonq
/ black_litterman.py
Created
July 25, 2023 02:48
Black-Litterman Model for Optimal Asset Allocation
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| import numpy as np | |
| from pypfopt.black_litterman import BlackLittermanModel | |
| from pypfopt.efficient_frontier import EfficientFrontier | |
| from pypfopt import plotting | |
| from openbb_terminal.sdk import openbb | |
| import seaborn as sns | |
| sns.set_theme() |
samsonq
/ arbitrage.py
Created
July 22, 2023 03:00
— forked from Valian/arbitrage.py
Short script for finding Binance Triangle arbitrage opportunities - requires python-binance installed
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| from collections import defaultdict | |
| from operator import itemgetter | |
| from time import time | |
| from binance.client import Client | |
| FEE = 0.0005 | |
| PRIMARY = ['ETH', 'USDT', 'BTC', 'BNB'] | |
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| def second_largest(n): | |
| first = n[0] | |
| second = n[1] | |
| if second > first: | |
| first, second = second, first | |
| for i in range(2, len(n)): | |
| if n[i] > first: | |
| first, second = n[i], first | |
| if second > first: | |
| first, second = second, first |
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| import numpy as np | |
| def monty_hall(): | |
| doors = np.array([1, 0, 0]) | |
| np.random.shuffle(doors) | |
| initial_choice = np.random.randint(len(doors)) | |
| goat_door = 0 | |
| for i in range(len(doors)): | |
| if i == initial_choice: |
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| def fib_dp(n, memo): | |
| if n in memo: | |
| return memo[n] | |
| else: | |
| fib_n = fib_dp(n-1, memo) + fib_dp(n-2, memo) | |
| memo[n] = fib_n | |
| return fib_n | |
| def fibonacci_dp(n): |
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| def find_poisoned_bottle(bottles): | |
| """ | |
| Find position of the poisonous wine bottle in 1000 bottles, using 10 strips, given that there | |
| is only 1 poisonous bottle of the 1000 bottles. | |
| The logic is to line up all 10 strips and represent them as binary numbers, with each strip in a position for the | |
| binary numbers. Then, each wine bottle is labeled from 1-1000. Since 2^10 is the first power of 2 that is greater than 1000, | |
| 10 strips are sufficient and we only need to test once with all 10 strips, and then wait 1 week for the test results to come back. | |
| Then, aligning the strips in the same order as before, there will be a sequence of positive and negative strips. Converting that | |
| sequence from binary (with positive as 1's and negative as 0's) back into decimal form, we will have the index of the poisoned |
samsonq
/ blackjack_simulation.py
Last active
June 28, 2022 22:01
Monte Carlo Simulation to Play Card Games (Poker, Blackjack, etc.)
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| import numpy as np | |
| import random |
samsonq
/ pi_circle_estimate.py
Created
April 16, 2022 16:32
Estimating Pi and Area of Circle with Monte Carlo Simulation
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| import numpy | |
| import math | |
| from matplotlib import pyplot as plt | |
| def pi_monte_carlo(num_simulations, radius=1): | |
| return ... | |
| if __name__ == "__main__": |
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| import math | |
| def selection_sort(a): | |
| for i in range(len(a)): | |
| curr_min = i | |
| for j in range(i, len(a)): | |
| if a[j] < a[curr_min]: | |
| curr_min = j | |
| if curr_min != i: | |
| a[i], a[curr_min] = a[curr_min], a[i] |
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