def gap(f):
r"""Calculate the gap percentage between the current open and
the previous close.
Parameters
----------
f : pandas.DataFrame
Dataframe with columns ``open`` and ``close``.
Returns
-------
new_column : pandas.Series (float)
The array containing the new feature.
References
----------
*A gap is a break between prices on a chart that occurs when the
price of a stock makes a sharp move up or down with no trading
occurring in between* [IP_GAP]_.
.. [IP_GAP] http://www.investopedia.com/terms/g/gap.asp
"""
c1 = 'open'
c2 = 'close[1]'
vexec(f, c2)
new_column = 100 * pchange2(f, c1, c2)
return new_column
def diminus(f, p=14):
r"""Calculate the Minus Directional Indicator (-DI).
Parameters
----------
f : pandas.DataFrame
Dataframe with columns ``high`` and ``low``.
p : int
The period over which to calculate the -DI.
Returns
-------
new_column : pandas.Series (float)
The array containing the new feature.
References
----------
*A component of the average directional index (ADX) that is used to
measure the presence of a downtrend. When the -DI is sloping downward,
it is a signal that the downtrend is getting stronger* [IP_NDI]_.
.. [IP_NDI] http://www.investopedia.com/terms/n/negativedirectionalindicator.asp
"""
tr = 'truerange'
vexec(f, tr)
atr = USEP.join(['atr', str(p)])
vexec(f, atr)
dmm = 'dmminus'
f[dmm] = dminus(f)
new_column = 100 * dminus(f).ewm(span=p).mean() / f[atr]
return new_column
def truehigh(f):
r"""Calculate the *True High* value.
Parameters
----------
f : pandas.DataFrame
Dataframe with columns ``high`` and ``low``.
Returns
-------
new_column : pandas.Series (float)
The array containing the new feature.
References
----------
*Today's high, or the previous close, whichever is higher* [TS_TR]_.
.. [TS_TR] http://help.tradestation.com/09_01/tradestationhelp/charting_definitions/true_range.htm
"""
c1 = 'low[1]'
vexec(f, c1)
c2 = 'high'
new_column = f.apply(c2max, axis=1, args=[c1, c2])
return new_column
def rsi(f, c, p=14):
r"""Calculate the Relative Strength Index (RSI).
Parameters
----------
f : pandas.DataFrame
Dataframe containing the column ``net``.
c : str
Name of the column in the dataframe ``f``.
p : int
The period over which to calculate the RSI.
Returns
-------
new_column : pandas.Series (float)
The array containing the new feature.
References
----------
*Developed by J. Welles Wilder, the Relative Strength Index (RSI) is a momentum
oscillator that measures the speed and change of price movements* [SC_RSI]_.
.. [SC_RSI] http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:relative_strength_index_rsi
"""
cdiff = 'net'
vexec(f, cdiff)
f['pval'] = upc(f, cdiff)
f['mval'] = dpc(f, cdiff)
upcs = ma(f, 'pval', p)
dpcs = ma(f, 'mval', p)
new_column = 100 - (100 / (1 + (upcs / dpcs)))
return new_column
def truelow(f):
r"""Calculate the *True Low* value.
Parameters
----------
f : pandas.DataFrame
Dataframe with columns ``high`` and ``low``.
Returns
-------
new_column : pandas.Series (float)
The array containing the new feature.
References
----------
*Today's low, or the previous close, whichever is lower* [TS_TR]_.
"""
c1 = 'high[1]'
vexec(f, c1)
c2 = 'low'
new_column = f.apply(c2min, axis=1, args=[c1, c2])
return new_column
def adx(f, p=14):
r"""Calculate the Average Directional Index (ADX).
Parameters
----------
f : pandas.DataFrame
Dataframe with all columns required for calculation. If you
are applying ADX through ``vapply``, then these columns are
calculated automatically.
p : int
The period over which to calculate the ADX.
Returns
-------
new_column : pandas.Series (float)
The array containing the new feature.
References
----------
The Average Directional Movement Index (ADX) was invented by J. Welles
Wilder in 1978 [WIKI_ADX]_. Its value reflects the strength of trend in any
given instrument.
.. [WIKI_ADX] https://en.wikipedia.org/wiki/Average_directional_movement_index
"""
c1 = 'diplus'
vexec(f, c1)
c2 = 'diminus'
vexec(f, c2)
# calculations
dip = f[c1]
dim = f[c2]
didiff = abs(dip - dim)
disum = dip + dim
new_column = 100 * didiff.ewm(span=p).mean() / disum
return new_column
def trade_system(model, system, space, intraday, name, quantity):
r"""Trade the given system.
Parameters
----------
model : alphapy.Model
The model object with specifications.
system : alphapy.System
The long/short system to run.
space : alphapy.Space
Namespace of instrument prices.
intraday : bool
If True, then run an intraday system.
name : str
The symbol to trade.
quantity : float
The amount of the ``name`` to trade, e.g., number of shares
Returns
-------
tradelist : list
List of trade entries and exits.
Other Parameters
----------------
Frame.frames : dict
All of the data frames containing price data.
"""
# Unpack the model data.
directory = model.specs['directory']
extension = model.specs['extension']
separator = model.specs['separator']
# Unpack the system parameters.
longentry = system.longentry
shortentry = system.shortentry
longexit = system.longexit
shortexit = system.shortexit
holdperiod = system.holdperiod
scale = system.scale
# Determine whether or not this is a model-driven system.
entries_and_exits = [longentry, shortentry, longexit, shortexit]
active_signals = [x for x in entries_and_exits if x is not None]
use_model = False
for signal in active_signals:
if any(x in signal for x in ['phigh', 'plow']):
use_model = True
# Read in the price frame
pf = Frame.frames[frame_name(name, space)].df
# Use model output probabilities as input to the system
if use_model:
# get latest probabilities file
probs_dir = SSEP.join([directory, 'output'])
file_path = most_recent_file(probs_dir, 'probabilities*')
file_name = file_path.split(SSEP)[-1].split('.')[0]
# read the probabilities frame and trim the price frame
probs_frame = read_frame(probs_dir, file_name, extension, separator)
pf = pf[-probs_frame.shape[0]:]
probs_frame.index = pf.index
probs_frame.columns = ['probability']
# add probability column to price frame
pf = pd.concat([pf, probs_frame], axis=1)
# Evaluate the long and short events in the price frame
for signal in active_signals:
vexec(pf, signal)
# Initialize trading state variables
inlong = False
inshort = False
h = 0
p = 0
q = quantity
tradelist = []
# Loop through prices and generate trades
for dt, row in pf.iterrows():
# get closing price
c = row['close']
if intraday:
bar_number = row['bar_number']
end_of_day = row['end_of_day']
# evaluate entry and exit conditions
lerow = row[longentry] if longentry else None
serow = row[shortentry] if shortentry else None
lxrow = row[longexit] if longexit else None
sxrow = row[shortexit] if shortexit else None
# process the long and short events
if lerow:
if p < 0:
# short active, so exit short
tradelist.append((dt, [name, Orders.sx, -p, c]))
inshort = False
h = 0
p = 0
if p == 0 or scale:
# go long (again)
tradelist.append((dt, [name, Orders.le, q, c]))
inlong = True
p = p + q
elif serow:
if p > 0:
# long active, so exit long
tradelist.append((dt, [name, Orders.lx, -p, c]))
inlong = False
h = 0
p = 0
if p == 0 or scale:
# go short (again)
tradelist.append((dt, [name, Orders.se, -q, c]))
inshort = True
p = p - q
# check exit conditions
if inlong and h > 0 and lxrow:
# long active, so exit long
tradelist.append((dt, [name, Orders.lx, -p, c]))
inlong = False
h = 0
p = 0
if inshort and h > 0 and sxrow:
# short active, so exit short
tradelist.append((dt, [name, Orders.sx, -p, c]))
inshort = False
h = 0
p = 0
# if a holding period was given, then check for exit
if holdperiod and h >= holdperiod:
if inlong:
tradelist.append((dt, [name, Orders.lh, -p, c]))
inlong = False
if inshort:
tradelist.append((dt, [name, Orders.sh, -p, c]))
inshort = False
h = 0
p = 0
# increment the hold counter
if inlong or inshort:
h += 1
if intraday and end_of_day:
if inlong:
# long active, so exit long
tradelist.append((dt, [name, Orders.lx, -p, c]))
inlong = False
if inshort:
# short active, so exit short
tradelist.append((dt, [name, Orders.sx, -p, c]))
inshort = False
h = 0
p = 0
return tradelist