def construct_signal(self, spot_df, spot_df2, tech_params, br):
##### FILL IN WITH YOUR OWN SIGNALS
# use technical indicator to create signals
# (we could obviously create whatever function we wanted for generating the signal dataframe)
tech_ind = TechIndicator()
tech_ind.create_tech_ind(spot_df, 'SMA', tech_params); signal_df = tech_ind.get_signal()
return signal_df
def construct_signal(self, spot_df, spot_df2, tech_params, br, run_in_parallel=False):
##### FILL IN WITH YOUR OWN SIGNALS
# Use technical indicator to create signals
# (we could obviously create whatever function we wanted for generating the signal dataframe)
tech_ind = TechIndicator()
tech_ind.create_tech_ind(spot_df, 'SMA', tech_params); signal_df = tech_ind.get_signal()
return signal_df
def construct_backtest(ticker, vendor_ticker, sma_period, data_source, start_date, quandl_api_key):
backtest = Backtest()
br = BacktestRequest()
# Set all the parameters for the backtest
br.start_date = start_date
br.finish_date = datetime.datetime.utcnow()
br.spot_tc_bp = 2.5 # 2.5 bps bid/ask spread
br.ann_factor = 252
tech_params = TechParams()
tech_params.sma_period = sma_period
indicator = 'SMA'
md_request = MarketDataRequest(
start_date=start_date,
finish_date=datetime.date.today(),
freq='daily',
data_source=data_source,
tickers=ticker,
fields=['close'],
vendor_tickers=vendor_ticker,
quandl_api_key=quandl_api_key)
market = Market(market_data_generator=MarketDataGenerator())
# Download the market data (the asset we are trading is also
# being used to generate the signal)
asset_df = market.fetch_market(md_request)
spot_df = asset_df
# Use technical indicator to create signals
# (we could obviously create whatever function we wanted for generating the signal dataframe)
# However, finmarketpy has some technical indicators built in (and some signals too)
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
backtest.calculate_trading_PnL(br, asset_df, signal_df, None, False)
# Get the returns and signals for the portfolio
port = backtest.portfolio_cum()
port.columns = [indicator + ' = ' + str(tech_params.sma_period) + ' ' + str(backtest.portfolio_pnl_desc()[0])]
signals = backtest.portfolio_signal()
# returns = backtest.pnl()
return port, signals
fields = ['close'], # which fields to download
vendor_tickers = vendor_tickers, # ticker (Quandl)
vendor_fields = ['close'], # which Bloomberg fields to download
cache_algo = 'internet_load_return') # how to return data
market = Market(market_data_generator=MarketDataGenerator())
asset_df = market.fetch_market(md_request)
spot_df = asset_df
logger.info("Running backtest...")
# use technical indicator to create signals
# (we could obviously create whatever function we wanted for generating the signal dataframe)
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
backtest.calculate_trading_PnL(br, asset_df, signal_df)
port = backtest.get_cumportfolio()
port.columns = [indicator + ' = ' + str(tech_params.sma_period) + ' ' + str(backtest.get_portfolio_pnl_desc()[0])]
signals = backtest.get_portfolio_signal()
# print the last positions (we could also save as CSV etc.)
print(signals.tail(1))
style = Style()
style.title = "FX trend strategy"
style.source = 'Quandl'
style.scale_factor = 1
style.file_output = 'fx-trend-example.png'
fields=['close'], # which fields to download
vendor_tickers=vendor_tickers, # ticker (Quandl)
vendor_fields=['close'], # which Bloomberg fields to download
cache_algo='internet_load_return') # how to return data
market = Market(market_data_generator=MarketDataGenerator())
asset_df = market.fetch_market(md_request)
spot_df = asset_df
logger.info("Running backtest...")
# use technical indicator to create signals
# (we could obviously create whatever function we wanted for generating the signal dataframe)
tech_ind = TechIndicator()
tech_ind.create_tech_ind(spot_df, indicator, tech_params)
signal_df = tech_ind.get_signal()
contract_value_df = None
# use the same data for generating signals
backtest.calculate_trading_PnL(br,
asset_df,
signal_df,
contract_value_df,
run_in_parallel=False)
port = backtest.portfolio_cum()
port.columns = [
indicator + ' = ' + str(tech_params.sma_period) + ' ' +
str(backtest.portfolio_pnl_desc()[0])
]
from finmarketpy.economics import TechIndicator, TechParams
from findatapy.util.loggermanager import LoggerManager
logger = LoggerManager().getLogger(__name__)
chart = Chart(engine='bokeh')
tech_ind = TechIndicator()
###### Simple example loading local data and using finmarketpy engine
# Load data from local file
df = pd.read_csv("/Volumes/Data/s&p500.csv", index_col=0, parse_dates=['Date'],
date_parser=lambda x: pd.datetime.strptime(x, '%Y-%m-%d'))
# Calculate Volume Weighted Average Price (VWAP)
tech_params = TechParams()
tech_ind.create_tech_ind(df, 'VWAP', tech_params)
df = tech_ind.get_techind()
print(df)
style = Style()
style.title = 'S&P500 VWAP'
style.scale_factor = 2
df = tech_ind.get_techind()
chart.plot(df, style=style)
if run_example == 1 or run_example == 0:
# Downloaded S&P500
md_request = MarketDataRequest(
start_date="01 Jan 2000", # start date
data_source='quandl', # use Quandl as data source
tickers=['S&P500'],
fields=['close', 'open', 'high', 'low'], # which fields to download
vendor_tickers=['YAHOO/INDEX_GSPC'], # ticker (Bloomberg)
vendor_fields=['close', 'open', 'high',
'low'], # which Bloomberg fields to download
cache_algo='internet_load_return') # how to return data
df = market.fetch_market(md_request)
print(df)
tech_params = TechParams()
tech_params.atr_period = 14
tech_ind.create_tech_ind(df, 'ATR', tech_params)
style = Style()
style.title = 'S&P500 ATR'
style.scale_factor = 2
style.file_output = "sp500.png"
style.source = 'Quandl/Yahoo'
df = tech_ind.get_techind()
chart.plot(df, style=style)
from finmarketpy.economics import TechIndicator, TechParams
from findatapy.util.loggermanager import LoggerManager
logger = LoggerManager().getLogger(__name__)
chart = Chart(engine='bokeh')
tech_ind = TechIndicator()
# Load data from local file
df = pd.read_csv("/Volumes/Data/s&p500.csv", index_col=0, parse_dates=['Date'],
date_parser=lambda x: pd.datetime.strptime(x, '%Y-%m-%d'))
# Calculate Volume Weighted Average Price (VWAP)
tech_params = TechParams()
tech_ind.create_tech_ind(df, 'VWAP', tech_params)
df = tech_ind.get_techind()
print(df)
style = Style()
style.title = 'S&P500 VWAP'
style.scale_factor = 2
df = tech_ind.get_techind()
chart.plot(df, style=style)
###### fetch data from Quandl for BoE rate (using Bloomberg data)
if run_example == 1 or run_example == 0:
# downloaded S&P500
md_request = MarketDataRequest(
start_date = "01 Jan 2000", # start date
data_source = 'quandl', # use Quandl as data source
tickers = ['S&P500'],
fields = ['close', 'open', 'high', 'low'], # which fields to download
vendor_tickers = ['YAHOO/INDEX_GSPC'], # ticker (Bloomberg)
vendor_fields = ['close', 'open', 'high', 'low'], # which Bloomberg fields to download
cache_algo = 'internet_load_return') # how to return data
df = market.fetch_market(md_request)
print(df)
tech_params = TechParams()
tech_params.atr_period = 14
tech_ind.create_tech_ind(df, 'ATR', tech_params)
style = Style()
style.title = 'S&P500 ATR'
style.scale_factor = 2
style.file_output = "sp500.png"
style.source = 'Quandl/Yahoo'
df = tech_ind.get_techind()
chart.plot(df, style=style)