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Release script (beta)
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codez0mb1e
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e1cd3ab1b6
@ -10,21 +10,20 @@ Description:
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# core
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import sys
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import warnings
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from IPython import sys_info
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# data science
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import pandas as pd
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import numpy as np
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from scipy.stats import norm
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from IPython import sys_info
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# Cloud integration
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from azureml.core import Workspace, Dataset, ComputeTarget, VERSION as aml_version
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print(f'Azure ML SDK v{aml_version}')
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# plots
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import matplotlib.pyplot as plt
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import seaborn as sns
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# Cloud integration
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from azureml.core import Workspace, Dataset, VERSION as aml_version
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print(f'Azure ML SDK v{aml_version}')
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# show info about python env
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print(sys_info())
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warnings.filterwarnings("ignore")
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@ -33,21 +32,24 @@ warnings.filterwarnings("ignore")
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# %% Set params ----
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symbols = ['USD/CHF', 'USD/CNY', 'USD/EUR', 'USD/GBP', 'USD/HKD', 'USD/JPY', 'USD/KZT', 'USD/RUB']
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n_days = int(252) # US market has 252 trading days in a year
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n_iterations = int(1e4)
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n_days = int(252) # US market has 252 trading days in a year
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n_simulations = int(1e4)
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# %% Connect to Azure ML workspace
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subscription_id = '9aef4ce1-e591-4870-9443-0b0eb98df2aa'
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resource_group = 'ai-bootcamp-rg'
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workspace_name = 'portf-opt-ws'
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# %% Connect to Azure ML workspace ----
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ws = Workspace.from_config()
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print(f"Connected to *{ws.get_details()['friendlyName']}* workspace in *{ws.get_details()['location']}*.")
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workspace = Workspace(subscription_id, resource_group, workspace_name) # Workspace.from_config()
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print(f"Connected to *{workspace.get_details()['friendlyName']}* workspace in *{workspace.get_details()['location']}*.")
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print('Compute Targets:')
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for compute_name in ws.compute_targets:
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compute = ws.compute_targets[compute_name]
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print('\t', compute.name, ':', compute.type)
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# > htop
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# %%
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currencies_ds = Dataset.get_by_name(workspace, name='Currencies')
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# %% Load dateset ----
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currencies_ds = Dataset.get_by_name(ws, name='Currencies')
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currencies_ds.to_pandas_dataframe()
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print(f'Dataset name: {currencies_ds.name}. Description: {currencies_ds.description}.')
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@ -85,7 +87,7 @@ pd.concat([
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])
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# %% Calculate Return
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# %% Calculate Return ----
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def get_returns(close_prices) -> pd.Series:
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return (close_prices/close_prices.shift()) - 1
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@ -96,8 +98,7 @@ usdrub_df['return'] = get_returns(usdrub_df['close'])
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usdrub_df[['close', 'diff', 'return']].tail(10)
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# %% Calculate LogReturn
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# %% Calculate LogReturn ----
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def get_log_returns(return_prices) -> pd.Series:
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return np.log(1 + return_prices)
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@ -105,7 +106,7 @@ usdrub_df['log_return'] = usdrub_df['return'].apply(lambda x: get_log_returns(x)
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usdrub_df[['close', 'diff', 'return', 'log_return']].tail(10)
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# %% Simulate possible LogReturns
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# %% Simulate possible LogReturns ----
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def get_simulated_returns(log_returns: pd.Series, n_days: int, n_iterations: int) -> pd.Series:
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u = log_returns.mean()
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@ -114,17 +115,17 @@ def get_simulated_returns(log_returns: pd.Series, n_days: int, n_iterations: int
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drift = u - (0.5*var)
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Z = norm.ppf(np.random.rand(n_days, n_iterations))
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return np.exp(drift + stdev*Z)
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usdrub_simulated_returns = get_simulated_returns(
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usdrub_df['log_return'].dropna(),
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n_days,
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n_iterations)
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usdrub_df['log_return'].dropna(),
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n_days,
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n_simulations)
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assert(
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usdrub_simulated_returns.shape == (n_days, n_iterations)
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usdrub_simulated_returns.shape == (n_days, n_simulations)
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and (usdrub_simulated_returns > 0).all()
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and (usdrub_simulated_returns < 2).all()
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)
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@ -132,10 +133,10 @@ assert(
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# %% Monte carlo simulation evaluation ----
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def get_breakeven_prob(pred, threshold: float = 0.) -> pd.Series:
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def get_breakeven_prob(pred, risk_free_rate: float = 0.02) -> pd.Series:
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"""
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Calculation of the probability of a stock being above a certain threshold,
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which can be defined as a value (final stock price) or return rate (percentage change)
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Calculation of the probability of a stock being above a certain threshold,
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which can be defined as a value (final stock price) or return rate (percentage change).
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"""
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init_pred = pred.iloc[0, 0]
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@ -143,21 +144,21 @@ def get_breakeven_prob(pred, threshold: float = 0.) -> pd.Series:
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pred_list = list(pred)
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over = [(p*100)/init_pred for p in pred_list if ((p-init_pred)*100)/init_pred >= threshold]
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less = [(p*100)/init_pred for p in pred_list if ((p-init_pred)*100)/init_pred < threshold]
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over = [(p*100)/init_pred for p in pred_list if ((p-init_pred)*100)/init_pred >= risk_free_rate]
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less = [(p*100)/init_pred for p in pred_list if ((p-init_pred)*100)/init_pred < risk_free_rate]
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return len(over)/(len(over) + len(less))
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def evaluate_simulation(simulated_returns: pd.Series, last_actual_price: float, n_days: int, plot = True) -> pd.DataFrame:
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"""
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"""
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Evaluate Monte-Carlo simulations result
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"""
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# Create empty matrix
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price_list = np.zeros_like(simulated_returns)
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# Put the last actual price in the first row
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# Put the last actual price in the first row,
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# and calculate the price of each day
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price_list[0] = last_actual_price
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for t in range(1, n_days):
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@ -169,52 +170,70 @@ def evaluate_simulation(simulated_returns: pd.Series, last_actual_price: float,
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# Plot
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if plot == True:
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x = price_df.iloc[-1]
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fig, ax = plt.subplots(1, 2, figsize=(14,4))
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fig, ax = plt.subplots(1, 2, figsize=(14, 4))
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sns.distplot(x, ax=ax[0])
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sns.distplot(x, hist_kws={'cumulative': True}, kde_kws={'cumulative': True}, ax=ax[1])
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plt.xlabel('Stock Price')
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plt.show()
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print(f'Investment period: {n_days-1} days')
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print(f'Expected Value: {round(price_df.iloc[-1].mean(), 2)} per USD')
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print(f'Return: {round(100*(price_df.iloc[-1].mean() - price_list[0,1]) /price_df.iloc[-1].mean(), 2)}%')
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print(f'Probability of Breakeven: {get_breakeven_prob(price_df)}')
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print('Results:')
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print(f'\tInvestment period: {n_days-1} days')
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print(f'\tExpected Value: {round(price_df.iloc[-1].mean(), 2)} per USD')
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print(f'\tReturn: {round(100*(price_df.iloc[-1].mean() - price_list[0,1])/price_df.iloc[-1].mean(), 2)}%')
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print(f'\tProbability of Breakeven: {get_breakeven_prob(price_df)}')
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return price_df
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# %% Run Monte carlo simulation and estimate result
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# %% Run Monte carlo simulation and estimate result ----
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usdrub_mc_simulation_df = evaluate_simulation(
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usdrub_simulated_returns,
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usdrub_simulated_returns,
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last_actual_price = usdrub_df['close'].tail(1),
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n_days = n_days)
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plt.figure(figsize=(10,6))
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plt.plot(usdrub_mc_simulation_df.sample(20, axis='columns'))
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plt.figure(figsize=(10, 6))
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plt.plot(usdrub_mc_simulation_df.sample(10, axis='columns'))
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plt.title('USD/RUB Price Simulation')
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plt.xlabel('Days')
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plt.ylabel('RUB per $1')
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plt.ylim(10, 300)
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plt.show()
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# %% Monte Carlo simulation pipeline for multiple tokens ----
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# 0. set simulation params
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n_simulations = int(1e4)
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# 1. prepare
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n_iterations = int(1e4) #! WARN: set simulations number
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quotes_data = [quotes_df.query('symbol == @s') for s in quotes_df.symbol.unique()]
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symbols_list = [df.symbol.unique() for df in quotes_data]
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# 2. simulate
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returns_data = [get_returns(df['close']) for df in quotes_data]
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log_returns_data = [get_log_returns(r) for r in returns_data]
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simulated_returns_data = [get_simulated_returns(lr, n_days, n_iterations) for lr in log_returns_data]
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simulated_returns_data = [get_simulated_returns(lr, n_days, n_simulations) for lr in log_returns_data]
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assert(
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len(quotes_data) > 0
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and len(quotes_data) == len(symbols_list) == len(returns_data) == len(log_returns_data) == len(simulated_returns_data)
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)
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# 3. evaluate
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for i in range(len(simulated_returns_data)):
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print(f'---- Starting Monte-Carlo simulation for {symbols[i]} symbol... ----')
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prices_ms = evaluate_simulation(simulated_returns_data[i], quotes_data[i]['close'].tail(1), n_days, plot=True)
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for i in range(len(symbols_list)):
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print(f'---- Starting Monte-Carlo simulation for {symbols_list[i]} symbol ----')
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plt.figure(figsize=(10,6))
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plt.plot(prices_ms.iloc[:, 1:50])
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prices_ms = evaluate_simulation(simulated_returns_data[i], quotes_data[i]['close'].tail(1), n_days, plot=False)
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plt.figure(figsize=(10, 6))
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plt.plot(prices_ms.sample(100, axis='columns'))
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plt.title(f'{symbols_list[i]} Price Simulation')
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plt.xlabel('Days')
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plt.ylabel('Amount per $1')
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plt.show()
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# %%
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# %% Completed ----
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gc()
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