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Minor changes
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@ -53,7 +53,7 @@ currencies_ds = Dataset.get_by_name(ws, name='Currencies')
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currencies_ds.to_pandas_dataframe()
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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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print(f'Dataset name: {currencies_ds.name}. Description: {currencies_ds.description}.')
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print(f'Size of Azure ML dataset: {sys.getsizeof(currencies_ds)} bytes.')
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print(f'Size of Azure ML dataset object: {sys.getsizeof(currencies_ds)} bytes.')
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# %% Preprocessing ----
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# %% Preprocessing ----
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@ -71,7 +71,7 @@ quotes_df = (currencies_ds
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.set_index('date')
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.set_index('date')
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.sort_values(by='date'))
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.sort_values(by='date'))
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quotes_df.head(10)
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quotes_df
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# %% Discover data ----
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# %% Discover data ----
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@ -80,35 +80,35 @@ quotes_df.groupby('symbol')['close'].agg(['count', 'last'])
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# %% USD/RUB dataset ----
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# %% USD/RUB dataset ----
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usdrub_df = quotes_df[quotes_df.symbol == 'USD/RUB']
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usdrub_df = quotes_df[quotes_df.symbol == 'USD/RUB']
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usdrub_df
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pd.concat([
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usdrub_df['close'].head(5),
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usdrub_df['close'].tail(5)
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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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def calc_returns(close_prices: pd.Series) -> pd.Series:
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"""Calculate Investment Return"""
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return (close_prices/close_prices.shift()) - 1
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return (close_prices/close_prices.shift()) - 1
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usdrub_df['diff'] = usdrub_df['close'].diff()
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usdrub_df['diff'] = usdrub_df['close'].diff()
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usdrub_df['return'] = get_returns(usdrub_df['close'])
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usdrub_df['return'] = calc_returns(usdrub_df['close'])
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usdrub_df[['close', 'diff', 'return']].tail(10)
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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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def calc_log_returns(return_prices: pd.Series) -> pd.Series:
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"""Calculate Log Return"""
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return np.log(1 + return_prices)
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return np.log(1 + return_prices)
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usdrub_df['log_return'] = usdrub_df['return'].apply(lambda x: get_log_returns(x))
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usdrub_df['log_return'] = usdrub_df['return'].apply(lambda x: calc_log_returns(x))
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usdrub_df[['close', 'diff', 'return', 'log_return']].tail(10)
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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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def calc_simulated_returns(log_returns: pd.Series, n_days: int, n_iterations: int) -> pd.Series:
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"""Calculate Simulated Return"""
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u = log_returns.mean()
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u = log_returns.mean()
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var = log_returns.var()
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var = log_returns.var()
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stdev = log_returns.std()
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stdev = log_returns.std()
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@ -119,7 +119,7 @@ def get_simulated_returns(log_returns: pd.Series, n_days: int, n_iterations: int
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return np.exp(drift + stdev*Z)
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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_simulated_returns = calc_simulated_returns(
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usdrub_df['log_return'].dropna(),
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usdrub_df['log_return'].dropna(),
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n_days,
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n_days,
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n_simulations)
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n_simulations)
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@ -140,9 +140,9 @@ def get_breakeven_prob(pred, risk_free_rate: float = 0.02) -> pd.Series:
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"""
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"""
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init_pred = pred.iloc[0, 0]
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init_pred = pred.iloc[0, 0]
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pred = pred.iloc[-1]
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pred_list = list(pred)
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last_pred = pred.iloc[-1]
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pred_list = list(last_pred)
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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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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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less = [(p*100)/init_pred for p in pred_list if ((p-init_pred)*100)/init_pred < risk_free_rate]
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@ -150,7 +150,7 @@ def get_breakeven_prob(pred, risk_free_rate: float = 0.02) -> pd.Series:
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return len(over)/(len(over) + len(less))
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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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def evaluate_simulation(simulated_returns: pd.Series, last_actual_price: float, n_days: int, plot: bool = True) -> pd.DataFrame:
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"""
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"""
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Evaluate Monte-Carlo simulations result
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Evaluate Monte-Carlo simulations result
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"""
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"""
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@ -211,9 +211,9 @@ 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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symbols_list = [df.symbol.unique() for df in quotes_data]
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# 2. simulate
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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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returns_data = [calc_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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log_returns_data = [calc_log_returns(r) for r in 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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simulated_returns_data = [calc_simulated_returns(lr, n_days, n_simulations) for lr in log_returns_data]
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assert(
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assert(
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len(quotes_data) > 0
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len(quotes_data) > 0
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