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This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -65,8 +65,8 @@ Sign up under https://py4at.pqp.io to access all the Jupyter Notebooks and execu <img src="http://hilpisch.com/pyalgo_cover_color.png" width=300px border=1px> Artificial Intelligence in Finance ---------------------------------- Our recent book about Artificial Intelligence in Finance (https://aiif.tpq.io). Sign up under https://aiif.pqp.io to access all the Jupyter Notebooks and execute them on our Quant Platform. -
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This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,298 @@ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<img src='http://hilpisch.com/tpq_logo.png' width=\"350px\" align=\"right\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# AI-Powered Algorithmic Trading with Python\n", "\n", "**ODSC London 2022**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dr. Yves J. Hilpisch | The Python Quants & The AI Machine\n", "\n", "https://tpq.io | https://aimachine.io | [@dyjh](http://twitter.com/dyjh)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a href=\"https://home.tpq.io/certificates/pyalgo\" target=\"_blank\"><img src=\"https://hilpisch.com/pyalgo_cover_shadow.png\" width=\"300px\" align=\"left\"></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Case Study: Momentum Strategy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Article\n", "\n", "You find the Medium article under http://bit.ly/100_algo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Oanda API " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import tpqoa\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "oanda = tpqoa.tpqoa('../oanda.cfg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Data " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "data = oanda.get_history(\n", " instrument='EUR_USD',\n", " start='2022-06-09',\n", " end='2022-06-10',\n", " granularity='M1',\n", " price='M'\n", ")\n", "data.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Strategy " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "data['r'] = np.log(data['c'] / data['c'].shift(1)) \n", "cols = []\n", "for momentum in [15, 30, 60, 120, 150]:\n", " col = f'p_{momentum}'\n", " data[col] = np.sign(data['r'].rolling(momentum).mean())\n", " cols.append(col)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pylab import plt\n", "plt.style.use('seaborn')\n", "%config InlineBackend.figure_format = 'svg'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# backtesting\n", "strats = ['r']\n", "for col in cols:\n", " strat = f'm_{col[2:]}' \n", " data[strat] = data[col].shift(1) * data['r']\n", " strats.append(strat)\n", "data[strats].dropna().cumsum().apply(np.exp).plot(cmap='coolwarm');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Trading Code" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "oanda.on_success??" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "oanda.stream_data('EUR_USD', stop=10) # streaming data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "oanda.create_order('EUR_USD', units=1000) # opening long position" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "oanda.create_order('EUR_USD', units=-1000) # closing long position" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simple Deployment" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class MomentumTrader(tpqoa.tpqoa):\n", " def __init__(self, config_file, momentum):\n", " super(MomentumTrader, self).__init__(config_file)\n", " self.momentum = momentum\n", " self.min_length = momentum + 1\n", " self.position = 0\n", " self.units = 10000\n", " self.tick_data = pd.DataFrame()\n", " def on_success(self, time, bid, ask):\n", " trade = False\n", " print(self.ticks, end=' ')\n", " df = pd.DataFrame({'b': bid, 'a': ask, 'm': (ask + bid) / 2},\n", " index=[pd.Timestamp(time).tz_localize(tz=None)])\n", " self.tick_data = pd.concat((self.tick_data, df))\n", " # resampling the tick data to 5 second intervals\n", " self.data = self.tick_data.resample('5s', label='right').last().ffill()\n", " self.data['r'] = np.log(self.data['m'] / self.data['m'].shift(1))\n", " self.data['m'] = self.data['r'].rolling(self.momentum).mean()\n", " self.data.dropna(inplace=True)\n", " if len(self.data) > self.min_length:\n", " self.min_length += 1\n", " # checking for long signal\n", " if self.data['m'].iloc[-2] > 0 and self.position in [0, -1]:\n", " o = oanda.create_order(self.stream_instrument,\n", " units=(1 - self.position) * self.units,\n", " suppress=True, ret=True)\n", " print('\\n*** GOING LONG ***')\n", " self.print_transactions(tid=int(o['id']) - 1)\n", " self.position = 1\n", " # checking for short signal\n", " if self.data['m'].iloc[-2] < 0 and self.position in [0, 1]:\n", " o = oanda.create_order(self.stream_instrument,\n", " units=-(1 + self.position) * self.units,\n", " suppress=True, ret=True)\n", " print('\\n*** GOING SHORT ***')\n", " self.print_transactions(tid=int(o['id']) - 1)\n", " self.position = -1 " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mt = MomentumTrader('../oanda.cfg', momentum=5)\n", "mt.stream_data('EUR_USD', stop=150)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pprint import pprint\n", "o = mt.create_order('EUR_USD', units=-mt.position * mt.units,\n", " suppress=True, ret = True)\n", "print('\\n*** POSITION CLOSED ***')\n", "mt.print_transactions(tid=int(o['id']) - 1)\n", "print('\\n')\n", "pprint(o)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src='http://hilpisch.com/tpq_logo.png' width=\"350px\" align=\"right\">" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 4 } This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,511 @@ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<img src='http://hilpisch.com/tpq_logo.png' width=\"350px\" align=\"right\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# AI-Powered Algorithmic Trading with Python\n", "\n", "**ODSC London 2022**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dr. Yves J. Hilpisch | The Python Quants & The AI Machine\n", "\n", "https://tpq.io | https://aimachine.io | [@dyjh](http://twitter.com/dyjh)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a href=\"https://home.tpq.io/certificates/pyalgo\" target=\"_blank\"><img src=\"https://hilpisch.com/aiif_cover_shadow.png\" width=\"300px\" align=\"left\"></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Case Study: AI-Powered Strategy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Oanda API " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import tpqoa\n", "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pylab import plt\n", "plt.style.use('seaborn')\n", "%config InlineBackend.figure_format = 'svg'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "warnings.simplefilter('ignore')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "oanda = tpqoa.tpqoa('../oanda.cfg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Data " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "data = oanda.get_history(\n", " instrument='BCO_USD',\n", " start='2022-06-09',\n", " end='2022-06-10',\n", " granularity='S5',\n", " price='M'\n", ")\n", "data.info()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data['r'] = np.log(data['c'] / data['c'].shift(1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data['d'] = np.sign(data['r'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Strategy " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lags = 3" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cols = list()\n", "for lag in range(1, lags + 1):\n", " col = f'lag_{lag}'\n", " data[col] = data['r'].shift(lag)\n", " cols.append(col)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data.dropna(inplace=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data['d'] = data['d'].astype(int)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train-Test Split" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "split = int(len(data) * 0.8)\n", "split" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "train = data.iloc[:split].copy()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mu, std = train.mean(), train.std()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "train_ = (train - mu) / std" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test = data.iloc[split:].copy()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test_ = (test - mu) / std" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training of the Model" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.neural_network import MLPClassifier\n", "from sklearn.metrics import accuracy_score" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model = MLPClassifier(hidden_layer_sizes=[24],\n", " shuffle=False,\n", " max_iter=500)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.fit(train_[cols], train['d'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "accuracy_score(train['d'], model.predict(train_[cols]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Testing of the Model" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test['p'] = model.predict(test_[cols])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "accuracy_score(test['d'], test['p'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test['s'] = test['p'] * test['r']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test[['r', 's']].sum().apply(np.exp)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test[['r', 's']].cumsum().apply(np.exp).plot();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Trading Code" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "oanda.on_success??" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "oanda.stream_data('BCO_USD', stop=10) # streaming data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "oanda.create_order('BCO_USD', units=100) # opening long position" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "oanda.create_order('BCO_USD', units=-100) # closing long position" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## Simple Deployment" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.predict(test[cols])[-1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class MLPTrader(tpqoa.tpqoa):\n", " def __init__(self, config_file, model, lags):\n", " super().__init__(config_file)\n", " self.model = model\n", " self.min_length = lags\n", " self.position = 0\n", " self.units = 100\n", " self.tick_data = pd.DataFrame()\n", " def on_success(self, time, bid, ask):\n", " trade = False\n", " print(self.ticks, end=' ')\n", " df = pd.DataFrame({'b': bid, 'a': ask, 'm': (ask + bid) / 2},\n", " index=[pd.Timestamp(time).tz_localize(tz=None)])\n", " self.tick_data = pd.concat((self.tick_data, df))\n", " # resampling the tick data to 5 second intervals\n", " self.data = self.tick_data.resample('5s', label='right').last().ffill()\n", " self.data['r'] = np.log(self.data['m'] / self.data['m'].shift(1))\n", " # self.data['m'] = self.data['r'].rolling(self.momentum).mean()\n", " self.data.dropna(inplace=True)\n", " if len(self.data) > self.min_length:\n", " self.min_length += 1\n", " # checking for long signal\n", " prediction = self.model.predict(\n", " self.data['m'].iloc[-lags-1:-1].values.reshape(1, -1))\n", " print(prediction)\n", " if prediction == 1 and self.position in [0, -1]:\n", " o = oanda.create_order(self.stream_instrument,\n", " units=(1 - self.position) * self.units,\n", " suppress=True, ret=True)\n", " print('\\n*** GOING LONG ***')\n", " self.print_transactions(tid=int(o['id']) - 1)\n", " self.position = 1\n", " # checking for short signal\n", " elif prediction == -1 and self.position in [0, 1]:\n", " o = oanda.create_order(self.stream_instrument,\n", " units=-(1 + self.position) * self.units,\n", " suppress=True, ret=True)\n", " print('\\n*** GOING SHORT ***')\n", " self.print_transactions(tid=int(o['id']) - 1)\n", " self.position = -1 " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mt = MLPTrader('../oanda.cfg', model, lags=lags)\n", "mt.stream_data('BCO_USD', stop=150)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pprint import pprint\n", "o = mt.create_order('BCO_USD', units=-mt.position * mt.units,\n", " suppress=True, ret=True)\n", "print('\\n*** POSITION CLOSED ***')\n", "mt.print_transactions(tid=int(o['id']) - 1)\n", "print('\\n')\n", "pprint(o)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src='http://hilpisch.com/tpq_logo.png' width=\"350px\" align=\"right\">" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 4 } This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,331 @@ # # Example Code for Algorithmic Strategy Deployment # on Oanda (https://oanda.com) # # (c) Dr. Yves J. Hilpisch # The Python Quants GmbH # # The code is for illustration purposes only. No warranties or representations # to the extent permitted by applicable law. The code does not # represent investment advice or a recommendation in any regard. # import re import math import uuid import numpy as np import pandas as pd from time import sleep from tpqoa import tpqoa from retrying import retry from dateutil import parser from abc import abstractmethod, ABCMeta from datetime import timedelta, datetime from model import Signal, Prediction, signal_queue valid_instruments = ['EUR_USD', 'BCO_USD'] valid_frequency = ['M1', 'M5', 'M10', 'M30'] def roundup(x, freq): return int(math.ceil(x / freq)) * freq def round_time(dt=None, date_delta=timedelta(minutes=1), to='average'): """ Round a datetime object to a multiple of a timedelta dt: datetime.datetime object, default now. dateDelta: timedelta object, we round to a multiple of this, default 1 minute. from: http://stackoverflow.com/questions/3463930/how-to-round-the-minute-of-a-datetime-object-python """ round_to = date_delta.total_seconds() if dt is None: dt = datetime.utcnow() seconds = (dt - timedelta(dt.minute)).second if to == 'up': rounding = (seconds + round_to) // round_to * round_to elif to == 'down': rounding = seconds // round_to * round_to else: rounding = (seconds + round_to / 2) // round_to * round_to dt = dt + timedelta(seconds=(rounding - seconds), microseconds=-dt.microsecond) return dt class BaseStrategy(metaclass=ABCMeta): def __init__(self, model_parameters, config): self.data_source = tpqoa(config) self.data = pd.DataFrame() self.live_price_data = pd.DataFrame() self.model_params = model_parameters self.n_bars = 120 self.first_run = True self.stop_model = False self.model_id = uuid.uuid4() self.signal_count = 0 self.feature_labels = set() # Important model params self.trading_quantity = None self.instrument = None self.frequency = None self.initialize() self.validate() ################################################################## # PRIVATE METHODS APPLICABLE FOR ALL STRATEGIES ################################################################## def _initialize_model_params(self): model_parameters = self.model_params for key, value in model_parameters.items(): setattr(self, key, value) @staticmethod def _get_time_unit_and_duration(freq): freq = re.findall(r'[A-Za-z]+|\d+', freq) min_or_sec = freq[0] duration = int(freq[1]) return duration, min_or_sec @retry(stop_max_attempt_number=7, wait_fixed=5000, wrap_exception=True) def _get_data(self, instrument, start, end, freq=None, price='M'): if freq is None: freq = self.frequency msg = f"Trying to get data from OANDA for {instrument} {start} {end}" msg += f" {freq} {price} at {datetime.utcnow()}" print(msg) start = parser.parse(start).strftime("%Y-%m-%d %H:%M:%S") end = parser.parse(end).strftime("%Y-%m-%d %H:%M:%S") raw_data = self.data_source.get_history( instrument, start, end, freq, price) return raw_data @staticmethod def _sleep_for_signal_gen(duration, signal_date): current_min = parser.parse(signal_date).minute current_second = parser.parse(signal_date).second next_min_level = roundup(current_min, duration) seconds_to_sleep = (((next_min_level - current_min) * 60) + 1 - current_second) if seconds_to_sleep > 0: print(f'signal gen thread: sleeping for {seconds_to_sleep} seconds') sleep(seconds_to_sleep) @staticmethod def _sleep_until_next_signal(duration, min_or_sec, signal_date): time_diff = (parser.parse(signal_date) - parser.parse(datetime.utcnow().isoformat() + 'Z')) seconds_diff = time_diff.seconds microseconds_diff = time_diff.microseconds # Sleep till the next min sleep_duration = duration if min_or_sec == 'M': sleep_duration = 60 * duration if seconds_diff < sleep_duration: msg = f'signal gen thread: sleeping for ' msg += f'{seconds_diff + microseconds_diff / 1000000} seconds' print(msg) sleep(seconds_diff + microseconds_diff / 1000000) def _publish_stop_signal(self): signal = Signal() signal.signal_id = uuid.uuid4() signal.model_id = self.model_id signal.instrument = self.instrument signal.prediction = Prediction.STOP self._publish_signal(signal) @staticmethod def _publish_signal(signal): print(f'Publishing Signal: {signal.signal_id}') signal_queue.put(signal) def _prepare_predict_data(self, original_signal_date): predict_data = pd.DataFrame() predict_data[self.instrument + '_close'] = self.live_price_data['c'] predict_data[self.instrument + '_open'] = self.live_price_data['o'] predict_data[self.instrument + '_high'] = self.live_price_data['h'] predict_data[self.instrument + '_low'] = self.live_price_data['l'] predict_data[self.instrument + '_volume'] = self.live_price_data['volume'] predict_data[self.instrument + '_date'] = self.live_price_data['time'] predict_data[self.instrument + '_return'] = \ np.log(predict_data[self.instrument + '_close'] / \ predict_data[self.instrument + '_close'].shift(1)) predict_data.dropna(inplace=True) predict_data.set_index(self.instrument + '_date', inplace=True) predict_data.loc[parser.parse(original_signal_date)] = 100 return predict_data def _get_signal_for_prediction(self, prediction): signal = Signal() signal.signal_id = uuid.uuid4() signal.model_id = self.model_id signal.instrument = self.instrument signal.prediction = prediction signal.quantity = self.trading_quantity return signal ################################################################## # PUBLIC METHODS THAT CAN BE OVERRIDDEN IN THE ACTUAL STRATEGY ################################################################## def set_n_bars(self, n_bars): # Override the number of candles to be fetched from data source. self.n_bars = n_bars def initialize(self): self._initialize_model_params() def validate(self): instrument = self.model_params['instrument'] if instrument not in valid_instruments: exit(f'{instrument} is not a valid/supported instruments') self.instrument = instrument frequency = self.model_params['frequency'] if frequency not in valid_frequency: exit(f'{frequency} is not a valid/supported frequency') self.frequency = frequency if 'trading_quantity' not in self.model_params: exit(f'trading quantity is mandatory') else: self.trading_quantity = self.model_params['trading_quantity'] if ('n_signals_to_gen' not in self.model_params) \ and ('stop_time' not in self.model_params): exit('stop_time or n_signals_to_gen required as exit condition') def generate_signal(self): signal_date = datetime.utcnow().isoformat()[:-7] + 'Z' duration, min_or_sec = self._get_time_unit_and_duration(self.frequency) if self.first_run is True and 'trade_immediately' in self.model_params and \ self.model_params['trade_immediately'] is True: self.first_run = False else: self.first_run = False if min_or_sec == 'M': self._sleep_for_signal_gen(duration, signal_date) signal_date = datetime.utcnow().isoformat()[:-7] + 'Z' print(f"generating signal now {datetime.utcnow()}") while True: try: self.check_for_stop_condition(signal_date) if self.stop_model is True: self._publish_stop_signal() break if min_or_sec == 'M': signal_date = round_time(parser.parse(signal_date), date_delta=timedelta(minutes=duration), to='up').isoformat()[:-6] + 'Z' signal = self.predict_for_time(signal_date) self.signal_count += 1 self._publish_signal(signal) if min_or_sec == 'M': self._sleep_for_signal_gen(duration, signal_date) sleep(2) self._sleep_until_next_signal(duration, min_or_sec, signal_date) signal_date = datetime.utcnow().isoformat()[:-7] + 'Z' except Exception as e: import traceback print(f'{traceback.format_exc()}') def check_for_stop_condition(self, signal_time): if 'n_signals_to_gen' in self.model_params: if self.signal_count >= self.model_params['n_signals_to_gen']: self.stop_model = True if 'stop_time' in self.model_params: stop_time = parser.parse( parser.parse(self.model_params['stop_time'] ).strftime("%Y-%m-%dT%H:%M:%SZ")) if stop_time <= signal_time: self.stop_model = True def predict_for_time(self, signal_date=None, is_first_run=False): original_signal_date = signal_date signal_date = parser.parse(signal_date) # * 3 is to avoid the lags being NaN time_periods_to_populate = self.n_bars start = self.get_starting_time(signal_date, time_periods_to_populate) raw_data = self._get_data(self.instrument, start.strftime("%Y-%m-%dT%H:%M:%SZ"), datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ"), freq=self.frequency, price='M') raw_data_len = len(raw_data) time_diff = (signal_date - start).seconds / 60 duration, min_or_sec = self._get_time_unit_and_duration(self.frequency) retry_count = 0 while raw_data_len < time_diff / duration: sleep(2) if retry_count > 6: print("Expected candles are {} got {}; stopping model.".format( str(int(time_diff / duration)), str(raw_data_len))) self.stop_model = True break raw_data = self._get_data(self.instrument, start.strftime("%Y-%m-%dT%H:%M:%SZ"), datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ"), freq=self.frequency, price='M') raw_data_len = len(raw_data) retry_count += 1 print("Expected candles are {} got {}.".format( str(int(time_diff / duration)), str(raw_data_len))) if self.stop_model is True: return raw_data.dropna(inplace=True) self.live_price_data = raw_data.reset_index() predict_data = self._prepare_predict_data(original_signal_date) self.custom_data_preparation(predict_data, False) prediction = self.on_signal(predict_data, signal_date) signal = self._get_signal_for_prediction(prediction) return signal def get_starting_time(self, signal_date, delta): duration, min_or_sec = self._get_time_unit_and_duration(self.frequency) if 'D' in self.frequency: return_date = signal_date - timedelta(days=delta * duration) elif 'M' in self.frequency: return_date = signal_date - timedelta(minutes=delta * duration) elif 'S' in self.frequency: return_date = signal_date - timedelta(seconds=delta * duration * 2) else: raise Exception(self.frequency + ' is not supported') return return_date @abstractmethod def custom_data_preparation(self, data, is_train_date): pass @abstractmethod def on_signal(self, predicted_data, signal_date): pass This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,37 @@ # # Example Code for Algorithmic Strategy Deployment # on Oanda (https://oanda.com) # # (c) Dr. Yves J. Hilpisch # The Python Quants GmbH # # The code is for illustration purposes only. No warranties or representations # to the extent permitted by applicable law. The code does not # represent investment advice or a recommendation in any regard. # import threading from model import signal_queue from processor import SignalProcessor from sma import sma # imports the trading strategy model_parameters = dict() model_parameters['instrument'] = 'EUR_USD' model_parameters['frequency'] = 'M1' model_parameters['trading_quantity'] = 100 model_parameters['n_signals_to_gen'] = 3 model_parameters['sma1'] = 3 model_parameters['sma2'] = 10 # model_parameters['trade_immediately'] = True if __name__ == '__main__': conf_file = '../oanda.cfg' threading.Thread(target=SignalProcessor(conf_file).listen_to_signal, daemon=True).start() strategy = sma(model_parameters, '../oanda.cfg') strategy.generate_signal() signal_queue.join() This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,42 @@ # # Example Code for Algorithmic Strategy Deployment # on Oanda (https://oanda.com) # # (c) Dr. Yves J. Hilpisch # The Python Quants GmbH # # The code is for illustration purposes only. No warranties or representations # to the extent permitted by applicable law. The code does not # represent investment advice or a recommendation in any regard. # import queue from enum import Enum signal_queue = queue.Queue() class Prediction(Enum): LONG = 1 SHORT = 2 NEUTRAL = 3 STOP = 4 class Signal: def __init__(self): self.model_id = None self.signal_id = None self.instrument = None self.prediction_time = None self.prediction = None self.quantity = None def __repr__(self): return str(self.__dict__) class SignalProcessingException(Exception): pass This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,146 @@ # # Example Code for Algorithmic Strategy Deployment # on Oanda (https://oanda.com) # # (c) Dr. Yves J. Hilpisch # The Python Quants GmbH # # The code is for illustration purposes only. No warranties or representations # to the extent permitted by applicable law. The code does not # represent investment advice or a recommendation in any regard. # from enum import Enum from tpqoa import tpqoa from threading import RLock from collections import defaultdict from model import signal_queue, Prediction, SignalProcessingException class StateAttrDef: QUANTITY = 'quantity' PRICE = 'price' REAL_PNL = 'real_pnl' class SignalProcessingType(Enum): GO_LONG = 1 GO_SHORT = 2 GO_LONG_FROM_SHORT = 3 GO_SHORT_FROM_LONG = 4 STOP = 5 NOOP = 6 class SignalProcessor: def __init__(self, conf_file='../oanda.cfg'): self.state = defaultdict(self._get_empty_state) self.oanda = tpqoa(conf_file) self.lock = RLock() @staticmethod def _get_empty_state(): empty_state = dict() empty_state[StateAttrDef.QUANTITY] = 0.0 empty_state[StateAttrDef.PRICE] = 0.0 empty_state[StateAttrDef.REAL_PNL] = 0.0 return empty_state def listen_to_signal(self): while True: try: signal = signal_queue.get() print(f'Processing signal: {signal.signal_id}') self.process_signal(signal) except Exception as e: import traceback print(f'{traceback.format_exc()}') def process_signal(self, signal): with self.lock: current_state = self.state[signal.instrument] print(f'Start processing signal {signal}.\nState={current_state}') signum_value = self._get_signum(current_state[StateAttrDef.QUANTITY]) processing_type = self._get_processing_type( signum_value, signal.prediction) print(f'Processing type: {processing_type}') state = self.place_trades_for_signal( signal, processing_type, current_state) print(f'Processed signal: {signal}.\nState={state}') def place_trades_for_signal(self, signal, processing_type, current_state): if SignalProcessingType.NOOP == processing_type: return current_state if processing_type in [SignalProcessingType.GO_LONG, SignalProcessingType.GO_LONG_FROM_SHORT]: net_qty = signal.quantity + abs(current_state[StateAttrDef.QUANTITY]) elif processing_type in [SignalProcessingType.GO_SHORT, SignalProcessingType.GO_SHORT_FROM_LONG]: net_qty = ((-1 * signal.quantity) - abs(current_state[StateAttrDef.QUANTITY])) elif processing_type == SignalProcessingType.STOP: net_qty = -1 * current_state[StateAttrDef.QUANTITY] else: msg = f'Invalid processing type {processing_type} encountered.' raise SignalProcessingException(msg) order_response = self._place_trade_for_ins(signal.instrument, net_qty) updated_state = self._update_state(order_response, current_state) return updated_state @staticmethod def _update_state(order_response, state): if 'tradesClosed' in order_response: for closed_trd in order_response['tradesClosed']: state[StateAttrDef.QUANTITY] += float(closed_trd['units']) state[StateAttrDef.REAL_PNL] += float(closed_trd['realizedPL']) if abs(state[StateAttrDef.QUANTITY]) <= 0.0001: state[StateAttrDef.PRICE] = 0.0 if 'tradeOpened' in order_response: open_trade = order_response['tradeOpened'] state[StateAttrDef.QUANTITY] += float(open_trade['units']) state[StateAttrDef.PRICE] += float(open_trade['price']) return state def _place_trade_for_ins(self, instrument, qty): response = self.oanda.create_order( instrument, qty, ret=True, suppress=True) # if type is not ORDER_FILL, there is some problem wih the order placement. if response['type'] != 'ORDER_FILL': raise SignalProcessingException(f'Error creating order: {response}.') return response @staticmethod def _get_processing_type(signum_value, prediction): if Prediction.STOP == prediction: return SignalProcessingType.STOP if signum_value == 0: return SignalProcessingType.GO_LONG if prediction == Prediction.LONG \ else SignalProcessingType.GO_SHORT if signum_value == 1: return SignalProcessingType.GO_SHORT_FROM_LONG \ if prediction == Prediction.SHORT \ else SignalProcessingType.NOOP if signum_value == -1: return SignalProcessingType.GO_LONG_FROM_SHORT \ if prediction == Prediction.LONG \ else SignalProcessingType.NOOP @staticmethod def _get_signum(x): if x == 0: return 0 else: return 1 if x > 0 else -1 This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,58 @@ # # Example Code for Algorithmic Strategy Deployment # on Oanda (https://oanda.com) # # (c) Dr. Yves J. Hilpisch # The Python Quants GmbH # # The code is for illustration purposes only. No warranties or representations # to the extent permitted by applicable law. The code does not # represent investment advice or a recommendation in any regard. # ''' This is a trading strategy template for an algorithmic trading strategy based on technical indicators that can be defined flexibly. ''' import numpy as np from model import Prediction from base import BaseStrategy class sma(BaseStrategy): """ This is the set of default model parameters. Override and add where applicable. """ def __init__(self, model_parameters, config): super().__init__(model_parameters, config) if model_parameters['sma1'] or model_parameters['sma2'] > self.n_bars: if model_parameters['sma1'] > model_parameters['sma2']: self.n_bars = model_parameters['sma1'] * 3 else: self.n_bars = model_parameters['sma2'] * 3 def custom_data_preparation(self, data, is_training_data): """ Add required data preparations here. """ prediction = self.instrument + '_prediction' data['sma1'] = (data[self.instrument + '_close'].rolling( self.sma1).mean().shift(1)) data['sma2'] = (data[self.instrument + '_close'].rolling( self.sma2).mean().shift(1)) data.dropna(inplace=True) data[prediction] = np.where(data['sma1'] > data['sma2'], 1, -1) def on_signal(self, predicted_data, signal_date): """ This method is called every time the strategy generates a signal. """ direction = predicted_data.loc[signal_date][ self.instrument + '_prediction'] if direction == -1: prediction = Prediction.SHORT else: prediction = Prediction.LONG return prediction -
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This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -14,6 +14,15 @@ Short Link ---------- http://bit.ly/odsc_ldn_2022 Slides ------------------ You find the slides under https://certificate.tpq.io/odsc_ldn_2022.pdf Blog Post --------- Read the blog post that gives you a quick overview and introduction: https://opendatascience.com/ai-powered-algorithmic-trading-with-python/ Abstract -------- @@ -31,12 +40,6 @@ Background knowledge needed Basic knowledge of Python and data science packages, such as NumPy, pandas, and matplotlib. Python Mastery in Finance Program ---------------------------------- -
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This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,104 @@ AI-Powered Algorithmic Trading with Python ===================================================== **ODSC London 2022 Half-Day Training** Dr. Yves J. Hilpisch<br> CEO The Python Quants | The AI Machine<br> Adjunct Professor of Computational Finance London, 15. June 2022 Short Link ---------- http://bit.ly/odsc_ldn_2022 Abstract -------- This half-day trading session covers the most important Python topics and skills to apply AI and Machine Learning (ML) to Algorithmic Trading. The session shows how to make use of the Oanda trading API (via a demo account) to retrieve data, stream data, place orders, etc. Building on this, a ML-based trading strategy is formulated and backtested. Finally, the trading strategy is transformed into an online trading algorithm and is deployed for real-time trading on the Oanda trading platform. Session Outline --------------- 1. Module: Setting up the Python and Oanda (paper) trading infrastructure 2. Module: Financial data logistics and backtesting of an ML-based algorithmic trading strategy 3. Module: Deployment of the ML-based algorithmic trading strategy in real-time Background knowledge needed --------------------------- Basic knowledge of Python and data science packages, such as NumPy, pandas, and matplotlib. Slides & Materials ------------------ To come. Python Mastery in Finance Program ---------------------------------- Platinum Package Program (Self-Paced): https://platinum.tpq.io Algorithmic Trading Online Bootcamp: https://platinum.tpq.io Financial Theory with Python --------------------------------- Our newest book about Financial Theory with Python (https://home.tpq.io/books/ftwp/). Sign up under https://finpy.pqp.io to access all the Jupyter Notebooks and execute them on our Quant Platform. <img src="https://hilpisch.com/finpy_cover.png" width=300px border=1px> Python for Algorithmic Trading --------------------------------- Our recent book about Python for Algorithmic Trading (https://py4at.tpq.io). Sign up under https://py4at.pqp.io to access all the Jupyter Notebooks and execute them on our Quant Platform. <img src="http://hilpisch.com/pyalgo_cover_color.png" width=300px border=1px> Artificial Intelligenc in Finance --------------------------------- Our recent book about Artificial Intelligence in Finance (https://aiif.tpq.io). Sign up under https://aiif.pqp.io to access all the Jupyter Notebooks and execute them on our Quant Platform. <img src="https://hilpisch.com/aiif_cover_color.png" width=300px border=1px> Python for Finance (2nd ed.) ---------------------------- Our standard reference book about Python for Finance (http://py4fi.tpq.io). Sign up under https://py4fi.pqp.io to access all the Jupyter Notebooks and execute them on our Quant Platform. <img src="https://hilpisch.com/images/py4fi_2nd.png" width=300px border=1px> Further Resources ----------------- * https://tpq.io * https://hilpisch.com * https://twitter.com/dyjh Python ------ If you have either **Miniconda** or **Anaconda** already installed, there is no need to install anything new. Otherwise, you might want to install **Miniconda** for your operating system: https://conda.io/en/master/miniconda.html Read more about the management of environments under: https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html Cloud ----- Use this link to get a 10 USD bonus on **[DigitalOcean](https://m.do.co/c/fbe512dd3dac)** when signing up for a new account. <img src="https://hilpisch.com/tpq_logo.png" width=250px>