end = datetime.datetime.utcnow()
start_date = end.replace(hour=0, minute=0, second=0, microsecond=0) # Returns a copy
time_series_request = TimeSeriesRequest(
start_date = start_date, # start date
finish_date = datetime.datetime.utcnow(), # finish date
freq = 'intraday', # intraday data
data_source = 'bloomberg', # use Bloomberg as data source
tickers = ['EURUSD'] , # ticker (Thalesians)
fields = ['close'], # which fields to download
vendor_tickers = ['EURUSD BGN Curncy'], # ticker (Bloomberg)
vendor_fields = ['close'], # which Bloomberg fields to download
cache_algo = 'internet_load_return') # how to return data
ltsf = LightTimeSeriesFactory()
df = ltsf.harvest_time_series(time_series_request)
df.columns = [x.replace('.close', '') for x in df.columns.values]
gp = GraphProperties()
gp.title = 'EURUSD stuff!'
gp.file_output = 'EURUSD.png'
gp.source = 'Thalesians/BBG (created with PyThalesians Python library)'
pf = PlotFactory()
pf.plot_line_graph(df, adapter = 'pythalesians', gp = gp)
pytwitter.update_status("check out my plot of EUR/USD!", picture = gp.file_output)
df_fred = ltsf.harvest_time_series(time_series_request)
df_fred.columns = [
x.replace('.close', '') for x in df_fred.columns.values
]
# convert to USD bn
# df_fred = (df_fred * 10000000)
df = df.join(df_fred, how="outer")
df['USDJPY'] = df['USDJPY'].ffill()
# data is in 100 million JPY, divide by 10 to get into 1000 million (ie. 1 billion)
# divide by USD/JPY spot to get into USD
df['USDJPY purchases (bn USD)'] = (df['USDJPY purchases (bn USD)'] /
df['USDJPY']) / 10
gp = GraphProperties()
gp.scale_factor = 3
gp.title = "BoJ USDJPY buying"
gp.file_output = "output_data/" + datetime.date.today().strftime("%Y%m%d") + " USDJPY BoJ intervention " \
+ str(gp.scale_factor) + ".png"
gp.source = 'Thalesians/BBG (created with PyThalesians Python library)'
gp.y_axis_2_series = ['USDJPY purchases (bn USD)']
gp.color_2_series = gp.y_axis_2_series
gp.color_2 = ['blue']
pf = PlotFactory()
pf.plot_line_graph(df, adapter='pythalesians', gp=gp)
# use the same data for generating signals
cash_backtest.calculate_trading_PnL(br, asset_df, signal_df)
port = cash_backtest.get_cumportfolio()
port.columns = [
indicator + ' = ' + str(tech_params.sma_period) + ' ' +
str(cash_backtest.get_portfolio_pnl_desc()[0])
]
signals = cash_backtest.get_porfolio_signal()
# print the last positions (we could also save as CSV etc.)
print(signals.tail(1))
pf = PlotFactory()
gp = GraphProperties()
gp.title = "Thalesians FX trend strategy"
gp.source = 'Thalesians/BBG (calc with PyThalesians Python library)'
gp.scale_factor = 1
gp.file_output = 'output_data/fx-trend-example.png'
pf.plot_line_graph(port, adapter='pythalesians', gp=gp)
###### backtest simple trend following strategy for FX spot basket
if True:
# for backtest and loading data
from pythalesians.market.requests.backtestrequest import BacktestRequest
from pythalesians.backtest.cash.cashbacktest import CashBacktest
from pythalesians.market.requests.timeseriesrequest import TimeSeriesRequest
from pythalesians.market.loaders.lighttimeseriesfactory import LightTimeSeriesFactory
from pythalesians.util.fxconv import FXConv
from pythalesians.timeseries.calcs.timeseriescalcs import TimeSeriesCalcs
import pandas
df.columns = [x.replace('.close', '') for x in df.columns.values]
short_dates = df[["EURUSDV1M", "USDJPYV1M"]]
long_dates = df[["EURUSDV1Y", "USDJPYV1Y"]]
short_dates, long_dates = short_dates.align(long_dates, join='left', axis = 0)
slope = pandas.DataFrame(data = short_dates.values - long_dates.values, index = short_dates.index,
columns = ["EURUSDV1M-1Y", "USDJPYV1M-1Y"])
# resample fand calculate average over month
slope_monthly = slope.resample('M').mean()
slope_monthly.index = [str(x.year) + '/' + str(x.month) for x in slope_monthly.index]
pf = PlotFactory()
gp = GraphProperties()
gp.source = 'Thalesians/BBG'
gp.title = 'Vol slopes in EUR/USD and USD/JPY recently'
gp.scale_factor = 2
gp.display_legend = True
gp.chart_type = 'bar'
gp.x_title = 'Dates'
gp.y_title = 'Pc'
# plot using Cufflinks
pf.plot_bar_graph(slope_monthly, adapter = 'cufflinks', gp = gp)
short_dates = df[["EURUSDV1M", "USDJPYV1M"]]
long_dates = df[["EURUSDV1Y", "USDJPYV1Y"]]
short_dates, long_dates = short_dates.align(long_dates,
join='left',
axis=0)
slope = pandas.DataFrame(data=short_dates.values - long_dates.values,
index=short_dates.index,
columns=["EURUSDV1M-1Y", "USDJPYV1M-1Y"])
# resample fand calculate average over month
slope_monthly = slope.resample('M').mean()
slope_monthly.index = [
str(x.year) + '/' + str(x.month) for x in slope_monthly.index
]
pf = PlotFactory()
gp = GraphProperties()
gp.source = 'Thalesians/BBG'
gp.title = 'Vol slopes in EUR/USD and USD/JPY recently'
gp.scale_factor = 2
gp.display_legend = True
gp.chart_type = 'bar'
gp.x_title = 'Dates'
gp.y_title = 'Pc'
# plot using Cufflinks
pf.plot_bar_graph(slope_monthly, adapter='cufflinks', gp=gp)
tech_ind = TechIndicator()
tech_ind.create_tech_ind(spot_df, indicator, tech_params); signal_df = tech_ind.get_signal()
# use the same data for generating signals
cash_backtest.calculate_trading_PnL(br, asset_df, signal_df)
port = cash_backtest.get_cumportfolio()
port.columns = [indicator + ' = ' + str(tech_params.sma_period) + ' ' + str(cash_backtest.get_portfolio_pnl_desc()[0])]
signals = cash_backtest.get_porfolio_signal()
# print the last positions (we could also save as CSV etc.)
print(signals.tail(1))
pf = PlotFactory()
gp = GraphProperties()
gp.title = "Thalesians FX trend strategy"
gp.source = 'Thalesians/BBG (calc with PyThalesians Python library)'
gp.scale_factor = 1
gp.file_output = 'output_data/fx-trend-example.png'
pf.plot_line_graph(port, adapter = 'pythalesians', gp = gp)
###### backtest simple trend following strategy for FX spot basket
if True:
# for backtest and loading data
from pythalesians.market.requests.backtestrequest import BacktestRequest
from pythalesians.backtest.cash.cashbacktest import CashBacktest
from pythalesians.market.requests.timeseriesrequest import TimeSeriesRequest
from pythalesians.market.loaders.lighttimeseriesfactory import LightTimeSeriesFactory
from pythalesians.util.fxconv import FXConv
from pythalesians.timeseries.calcs.timeseriescalcs import TimeSeriesCalcs