Dr. Yves J. Hilpisch
The Python Quants GmbH
Risk Disclaimer¶
Trading forex/CFDs on margin carries a high level of risk and may not be suitable for all investors as you could sustain losses in excess of deposits. Leverage can work against you. Due to the certain restrictions imposed by the local law and regulation, German resident retail client(s) could sustain a total loss of deposited funds but are not subject to subsequent payment obligations beyond the deposited funds. Be aware and fully understand all risks associated with the market and trading. Prior to trading any products, carefully consider your financial situation and experience level. Any opinions, news, research, analyses, prices, or other information is provided as general market commentary, and does not constitute investment advice. FXCM & TPQ will not accept liability for any loss or damage, including without limitation to, any loss of profit, which may arise directly or indirectly from use of or reliance on such information.
Speaker Disclaimer¶
The speaker is neither an employee, agent nor representative of FXCM and is therefore acting independently. The opinions given are their own, constitute general market commentary, and do not constitute the opinion or advice of FXCM or any form of personal or investment advice. FXCM assumes no responsibility for any loss or damage, including but not limited to, any loss or gain arising out of the direct or indirect use of this or any other content. Trading forex/CFDs on margin carries a high level of risk and may not be suitable for all investors as you could sustain losses in excess of deposits.
Some Basic Imports¶
import time
import numpy as np
import pandas as pd
import datetime as dt
import cufflinks as cf
from pylab import plt
cf.set_config_file(offline=True)
plt.style.use('seaborn')
%matplotlib inline
You can install fxcmpy via
pip install fxcmpy
The documentation is currently found under http://fxcmpy.tpq.io
Read the license & risk warning carefully.
import fxcmpy
fxcmpy.__version__
Connecting to the FXCM RESTful API¶
For this approach, a configuration file is needed (see Quick Start).
api = fxcmpy.fxcmpy(config_file='../fxcm.cfg')
instruments = api.get_instruments()
print(instruments)
Historical Data via RESTful API¶
candles = api.get_candles('USD/JPY', period='D1', number=10)
# 10 most recent days | daily
candles
start = dt.datetime(2017, 1, 1)
end = dt.datetime(2018, 1, 1)
candles = api.get_candles('USD/JPY', period='D1',
start=start, stop=end)
candles['askclose'].iplot()
The parameter period must be one of m1, m5, m15, m30, H1, H2, H3, H4, H6, H8, D1, W1 or M1.
# 50 most recent 1 minute bars
candles = api.get_candles('EUR/USD', period='m1', number=150)
candles.tail(10)
Retrieving Streaming Data¶
def output(data, dataframe):
print('%3d | %s | %s | %6.4f, %6.4f, %6.4f, %6.4f'
% (len(dataframe), data['Symbol'],
pd.to_datetime(int(data['Updated']), unit='ms'),
data['Rates'][0], data['Rates'][1],
data['Rates'][2], data['Rates'][3]))
api.subscribe_market_data('EUR/USD', (output,))
api.get_last_price('EUR/USD')
api.unsubscribe_market_data('EUR/USD')
Placing Orders via the RESTful API¶
api.get_open_positions()
order = api.create_market_buy_order('EUR/USD', 100)
sel = ['tradeId', 'amountK', 'currency', 'grossPL', 'isBuy']
api.get_open_positions()[sel]
order = api.create_market_buy_order('USD/JPY', 50)
api.get_open_positions()[sel]
order = api.create_market_sell_order('EUR/USD', 25)
order = api.create_market_buy_order('USD/JPY', 50)
api.get_open_positions()[sel]
order = api.create_market_sell_order('EUR/USD', 50)
api.get_open_positions()[sel]
api.close_all_for_symbol('USD/JPY')
api.get_open_positions()[sel]
api.close_all()
api.get_open_positions()
Backtesting AI-Based Algorithmic Trading Strategy¶
The following example is simplified and for illustration purposes only. Among others, it does not consider transactions costs or bid-ask spreads.
Data Retrieval¶
candles = api.get_candles('EUR/USD', period='m5',
start=dt.datetime(2018, 3, 26),
stop=dt.datetime(2018, 3, 30))
data = pd.DataFrame(candles[['askclose', 'bidclose']].mean(axis=1),
columns=['midclose'])
data.info()
data.iplot()
Feature Preparation¶
data['returns'] = np.log(data / data.shift(1))
lags = 5
cols = []
for lag in range(1, lags + 1):
col = 'lag_%s' % lag
data[col] = data['returns'].shift(lag)
cols.append(col)
cols
data.dropna(inplace=True)
# the "patterns" = 2 ** lags
np.digitize(data[cols], bins=[0])[:10]
2 ** len(cols)
Support Vector Machines¶
from sklearn import svm
model = svm.SVC(C=100)
Train Test Split¶
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
train_x, test_x, train_y, test_y = train_test_split(
data[cols].apply(lambda x: np.digitize(x, bins=[0])),
np.sign(data['returns']),
test_size=0.50, random_state=111)
train_x.sort_index(inplace=True)
train_y.sort_index(inplace=True)
test_x.sort_index(inplace=True)
test_y.sort_index(inplace=True)
Model Fitting & Prediction¶
model.fit(train_x, train_y)
train_pred = model.predict(train_x)
accuracy_score(train_y, train_pred)
test_pred = model.predict(test_x)
accuracy_score(test_y, test_pred)
Vectorized Backtesting¶
data['position'] = model.predict(data[cols].apply(lambda x: np.digitize(x, bins=[0])).dropna())
data['strategy'] = data['position'] * data['returns']
# in-sample | unleveraged | no bid-ask spread or transaction costs
data.loc[train_x.index][['returns', 'strategy']].cumsum().apply(np.exp).iplot()
# out-of-sample | unleveraged | no bid-ask spread or transaction costs
data.loc[test_x.index][['returns', 'strategy']].cumsum().apply(np.exp).iplot()
# number of trades
sum(data['position'].diff() != 0)
Automating A Simple Time-Based Algorithm¶
for _ in range(5):
print(50 * '=')
print('TRADE NO {}'.format(_))
order = api.create_market_buy_order('EUR/USD', 100)
time.sleep(1)
print('POSITIONS\n', api.get_open_positions()[sel])
time.sleep(7)
api.close_all_for_symbol('EUR/USD')
print('POSITIONS\n', api.get_open_positions())
time.sleep(7)