From 94cd341927aaf1dcb52d3f8c705b980dfe627313 Mon Sep 17 00:00:00 2001 From: jose-donato Date: Wed, 6 Apr 2022 23:07:58 +0100 Subject: [PATCH 1/5] added qa and pred to forex --- .../cryptocurrency/crypto_controller.py | 8 +- openbb_terminal/forex/forex_controller.py | 62 +- .../forex/prediction_techniques/__init__.py | 0 .../forex/prediction_techniques/pred_api.py | 33 + .../prediction_techniques/pred_controller.py | 716 ++++++++++++++++++ .../forex/quantitative_analysis/__init__.py | 0 .../quantitative_analysis/qa_controller.py | 704 +++++++++++++++++ scripts/test_forex_pred.openbb | 6 + scripts/test_forex_qa.openbb | 10 + .../forex/test_forex_controller.py | 4 +- .../test_forex_controller/test_print_help.txt | 2 + 11 files changed, 1537 insertions(+), 8 deletions(-) create mode 100644 openbb_terminal/forex/prediction_techniques/__init__.py create mode 100644 openbb_terminal/forex/prediction_techniques/pred_api.py create mode 100644 openbb_terminal/forex/prediction_techniques/pred_controller.py create mode 100644 openbb_terminal/forex/quantitative_analysis/__init__.py create mode 100644 openbb_terminal/forex/quantitative_analysis/qa_controller.py create mode 100644 scripts/test_forex_pred.openbb create mode 100644 scripts/test_forex_qa.openbb diff --git a/openbb_terminal/cryptocurrency/crypto_controller.py b/openbb_terminal/cryptocurrency/crypto_controller.py index 1612559156de..8ec1844df0aa 100644 --- a/openbb_terminal/cryptocurrency/crypto_controller.py +++ b/openbb_terminal/cryptocurrency/crypto_controller.py @@ -109,8 +109,8 @@ def print_help(self): > nft non-fungible tokens, e.g.: today drops{has_ticker_start} > dd due-diligence for loaded coin, e.g.: coin information, social media, market stats > ta technical analysis for loaded coin, e.g.: ema, macd, rsi, adx, bbands, obv -> pred prediction techniques e.g.: regression, arima, rnn, lstm, conv1d, monte carlo -> qa quantitative analysis, \t e.g.: decompose, cusum, residuals analysis[/menu] +> pred prediction techniques, e.g.: regression, arima, rnn, lstm, conv1d, monte carlo +> qa quantitative analysis e.g.: decompose, cusum, residuals analysis[/menu] {has_ticker_end} """ console.print(text=help_text, menu="Cryptocurrency") @@ -451,9 +451,7 @@ def call_ov(self, _): @log_start_end(log=logger) def call_defi(self, _): """Process defi command""" - from openbb_terminal.cryptocurrency.defi.defi_controller import ( - DefiController, - ) + from openbb_terminal.cryptocurrency.defi.defi_controller import DefiController self.queue = self.load_class(DefiController, self.queue) diff --git a/openbb_terminal/forex/forex_controller.py b/openbb_terminal/forex/forex_controller.py index d147cae499fd..665b8a3db8ba 100644 --- a/openbb_terminal/forex/forex_controller.py +++ b/openbb_terminal/forex/forex_controller.py @@ -29,7 +29,7 @@ class ForexController(BaseController): """Forex Controller class.""" CHOICES_COMMANDS = ["to", "from", "load", "quote", "candle", "resources"] - CHOICES_MENUS = ["ta", "oanda"] + CHOICES_MENUS = ["ta", "qa", "oanda", "pred"] PATH = "/forex/" FILE_PATH = os.path.join(os.path.dirname(__file__), "README.md") @@ -66,6 +66,8 @@ def print_help(self): candle show candle plot for loaded data[/cmds] [menu] > ta technical analysis for loaded coin, e.g.: ema, macd, rsi, adx, bbands, obv +> qa quantitative analysis, e.g.: decompose, cusum, residuals analysis +> pred prediction techniques e.g.: regression, arima, rnn, lstm, conv1d, monte carlo [/menu]{has_symbols_end} [info]Forex brokerages:[/info][menu] > oanda Oanda menu[/menu][/cmds] @@ -318,6 +320,64 @@ def call_ta(self, _): else: console.print("No currency pair data is loaded. Use 'load' to load data.\n") + @log_start_end(log=logger) + def call_pred(self, _): + """Process pred command""" + if obbff.ENABLE_PREDICT: + if self.from_symbol and self.to_symbol: + if self.data.empty: + console.print( + "No currency pair data is loaded. Use 'load' to load data.\n" + ) + else: + try: + from openbb_terminal.forex.prediction_techniques import ( + pred_controller, + ) + + self.queue = self.load_class( + pred_controller.PredictionTechniquesController, + self.from_symbol, + self.to_symbol, + self.data.index[0], + "1440min", + self.data, + self.queue, + ) + except ImportError: + logger.exception("Tensorflow not available") + console.print( + "[red]Run pip install tensorflow to continue[/red]\n" + ) + else: + console.print("No pair selected.\n") + else: + console.print( + "Predict is disabled. Check ENABLE_PREDICT flag on feature_flags.py", + "\n", + ) + + @log_start_end(log=logger) + def call_qa(self, _): + """Process qa command""" + if self.from_symbol and self.to_symbol: + if self.data.empty: + console.print( + "No currency pair data is loaded. Use 'load' to load data.\n" + ) + else: + from openbb_terminal.forex.quantitative_analysis import qa_controller + + self.queue = self.load_class( + qa_controller.QaController, + self.from_symbol, + self.to_symbol, + self.data, + self.queue, + ) + else: + console.print("No pair selected.\n") + # HELP WANTED! # TODO: Add news and reddit commands back # behavioural analysis and exploratory data analysis would be useful in the diff --git a/openbb_terminal/forex/prediction_techniques/__init__.py b/openbb_terminal/forex/prediction_techniques/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/openbb_terminal/forex/prediction_techniques/pred_api.py b/openbb_terminal/forex/prediction_techniques/pred_api.py new file mode 100644 index 000000000000..ea1d2fe64029 --- /dev/null +++ b/openbb_terminal/forex/prediction_techniques/pred_api.py @@ -0,0 +1,33 @@ +"""Pred context API.""" + +# flake8: noqa +# pylint: disable=unused-import + +# Menu commands +from openbb_terminal.common.prediction_techniques.ets_view import ( + display_exponential_smoothing as ets, +) +from openbb_terminal.common.prediction_techniques.knn_view import ( + display_k_nearest_neighbors as knn, +) +from openbb_terminal.common.prediction_techniques.regression_view import ( + display_regression as regression, +) +from openbb_terminal.common.prediction_techniques.arima_view import ( + display_arima as arima, +) +from openbb_terminal.common.prediction_techniques.neural_networks_view import ( + display_mlp as mlp, +) +from openbb_terminal.common.prediction_techniques.neural_networks_view import ( + display_rnn as rnn, +) +from openbb_terminal.common.prediction_techniques.neural_networks_view import ( + display_lstm as lstm, +) +from openbb_terminal.common.prediction_techniques.neural_networks_view import ( + display_conv1d as conv1d, +) +from openbb_terminal.common.prediction_techniques.mc_view import ( + display_mc_forecast as mc, +) diff --git a/openbb_terminal/forex/prediction_techniques/pred_controller.py b/openbb_terminal/forex/prediction_techniques/pred_controller.py new file mode 100644 index 000000000000..1bf460e1dfd6 --- /dev/null +++ b/openbb_terminal/forex/prediction_techniques/pred_controller.py @@ -0,0 +1,716 @@ +""" Prediction Controller """ +__docformat__ = "numpy" + +import argparse +import logging +from datetime import datetime +from typing import List + +import numpy as np +import pandas as pd +from prompt_toolkit.completion import NestedCompleter + +from openbb_terminal import feature_flags as obbff +from openbb_terminal.common.prediction_techniques import ( + arima_model, + arima_view, + ets_model, + ets_view, + knn_view, + mc_model, + mc_view, + neural_networks_view, + pred_helper, + regression_view, +) +from openbb_terminal.decorators import log_start_end +from openbb_terminal.helper_funcs import ( + EXPORT_ONLY_FIGURES_ALLOWED, + check_positive, + get_next_stock_market_days, + parse_known_args_and_warn, + valid_date, +) +from openbb_terminal.menu import session +from openbb_terminal.parent_classes import BaseController +from openbb_terminal.rich_config import console + +logger = logging.getLogger(__name__) + + +class PredictionTechniquesController(BaseController): + """Prediction Techniques Controller class""" + + CHOICES_COMMANDS = [ + "pick", + "ets", + "knn", + "regression", + "arima", + "mlp", + "rnn", + "lstm", + "conv1d", + "mc", + ] + + PATH = "/forex/pred/" + + def __init__( + self, + from_symbol: str, + to_symbol: str, + start: datetime, + interval: str, + data: pd.DataFrame, + queue: List[str] = None, + ): + """Constructor""" + super().__init__(queue) + + data["Returns"] = data["Adj Close"].pct_change() + data["LogRet"] = np.log(data["Adj Close"]) - np.log(data["Adj Close"].shift(1)) + data = data.rename(columns={"Adj Close": "AdjClose"}) + data = data.dropna() + + self.data = data + self.from_symbol = from_symbol + self.to_symbol = to_symbol + self.ticker = f"{from_symbol}/{to_symbol}" + self.start = start + self.interval = interval + self.target = "AdjClose" + + if session and obbff.USE_PROMPT_TOOLKIT: + choices: dict = {c: {} for c in self.controller_choices} + choices["pick"] = {c: {} for c in self.data.columns} + choices["ets"]["-t"] = {c: {} for c in ets_model.TRENDS} + choices["ets"]["-s"] = {c: {} for c in ets_model.SEASONS} + choices["arima"]["-i"] = {c: {} for c in arima_model.ICS} + choices["mc"]["--dist"] = {c: {} for c in mc_model.DISTRIBUTIONS} + self.completer = NestedCompleter.from_nested_dict(choices) + + def print_help(self): + """Print help""" + pair_info = f"{self.ticker} (from {self.start.strftime('%Y-%m-%d')})" + + help_text = f"""[cmds] + pick pick new target variable[/cmds] + +[param]Pair Loaded: [/param]{pair_info} +[param]Target Column: [/param]{self.target} + +[info]Models:[/info][cmds] + ets exponential smoothing (e.g. Holt-Winters) + knn k-Nearest Neighbors + regression polynomial regression + arima autoregressive integrated moving average + mlp MultiLayer Perceptron + rnn Recurrent Neural Network + lstm Long-Short Term Memory + conv1d 1D Convolutional Neural Network + mc Monte-Carlo simulations[/cmds] + """ + console.print(text=help_text, menu="Forex - Prediction Techniques") + + def custom_reset(self): + """Class specific component of reset command""" + if self.ticker: + return [ + "forex", + f"from {self.from_symbol}", + f"to {self.to_symbol}", + "load", + "pred", + ] + return [] + + @log_start_end(log=logger) + def call_pick(self, other_args: List[str]): + """Process pick command""" + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + add_help=False, + prog="pick", + description=""" + Change target variable + """, + ) + parser.add_argument( + "-t", + "--target", + dest="target", + choices=list(self.data.columns), + help="Select variable to analyze", + ) + if other_args and "-t" not in other_args and "-h" not in other_args: + other_args.insert(0, "-t") + + ns_parser = parse_known_args_and_warn( + parser, other_args, EXPORT_ONLY_FIGURES_ALLOWED + ) + if ns_parser: + self.target = ns_parser.target + console.print("") + + @log_start_end(log=logger) + def call_ets(self, other_args: List[str]): + """Process ets command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="ets", + description=""" + Exponential Smoothing, see https://otexts.com/fpp2/taxonomy.html + + Trend='N', Seasonal='N': Simple Exponential Smoothing + Trend='N', Seasonal='A': Exponential Smoothing + Trend='N', Seasonal='M': Exponential Smoothing + Trend='A', Seasonal='N': Holt’s linear method + Trend='A', Seasonal='A': Additive Holt-Winters’ method + Trend='A', Seasonal='M': Multiplicative Holt-Winters’ method + Trend='Ad', Seasonal='N': Additive damped trend method + Trend='Ad', Seasonal='A': Exponential Smoothing + Trend='Ad', Seasonal='M': Holt-Winters’ damped method + Trend component: N: None, A: Additive, Ad: Additive Damped + Seasonality component: N: None, A: Additive, M: Multiplicative + """, + ) + parser.add_argument( + "-d", + "--days", + action="store", + dest="n_days", + type=check_positive, + default=5, + help="prediction days.", + ) + parser.add_argument( + "-t", + "--trend", + action="store", + dest="trend", + choices=ets_model.TRENDS, + default="N", + help="Trend component: N: None, A: Additive, Ad: Additive Damped.", + ) + parser.add_argument( + "-s", + "--seasonal", + action="store", + dest="seasonal", + choices=ets_model.SEASONS, + default="N", + help="Seasonality component: N: None, A: Additive, M: Multiplicative.", + ) + parser.add_argument( + "-p", + "--periods", + action="store", + dest="seasonal_periods", + type=check_positive, + default=5, + help="Seasonal periods.", + ) + parser.add_argument( + "-e", + "--end", + action="store", + type=valid_date, + dest="s_end_date", + default=None, + help="The end date (format YYYY-MM-DD) to select - Backtesting", + ) + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED + ) + if ns_parser: + + if ns_parser.s_end_date: + + if ns_parser.s_end_date < self.data.index[0]: + console.print( + "Backtesting not allowed, since End Date is older than Start Date of historical data\n" + ) + + if ( + ns_parser.s_end_date + < get_next_stock_market_days( + last_stock_day=self.data.index[0], + n_next_days=5 + ns_parser.n_days, + )[-1] + ): + console.print( + "Backtesting not allowed, since End Date is too close to Start Date to train model\n" + ) + + ets_view.display_exponential_smoothing( + ticker=self.ticker, + values=self.data[self.target], + n_predict=ns_parser.n_days, + trend=ns_parser.trend, + seasonal=ns_parser.seasonal, + seasonal_periods=ns_parser.seasonal_periods, + s_end_date=ns_parser.s_end_date, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_knn(self, other_args: List[str]): + """Process knn command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="knn", + description=""" + K nearest neighbors is a simple algorithm that stores all + available cases and predict the numerical target based on a similarity measure + (e.g. distance functions). + """, + ) + parser.add_argument( + "-i", + "--input", + action="store", + dest="n_inputs", + type=check_positive, + default=40, + help="number of days to use as input for prediction.", + ) + parser.add_argument( + "-d", + "--days", + action="store", + dest="n_days", + type=check_positive, + default=5, + help="prediction days.", + ) + parser.add_argument( + "-j", + "--jumps", + action="store", + dest="n_jumps", + type=check_positive, + default=1, + help="number of jumps in training data.", + ) + parser.add_argument( + "-n", + "--neighbors", + action="store", + dest="n_neighbors", + type=check_positive, + default=20, + help="number of neighbors to use on the algorithm.", + ) + parser.add_argument( + "-e", + "--end", + action="store", + type=valid_date, + dest="s_end_date", + default=None, + help="The end date (format YYYY-MM-DD) to select for testing", + ) + parser.add_argument( + "-t", + "--test_size", + default=0.2, + dest="valid_split", + type=float, + help="Percentage of data to validate in sample", + ) + parser.add_argument( + "--no_shuffle", + action="store_false", + dest="no_shuffle", + default=True, + help="Specify if shuffling validation inputs.", + ) + ns_parser = parse_known_args_and_warn( + parser, other_args, EXPORT_ONLY_FIGURES_ALLOWED + ) + if ns_parser: + knn_view.display_k_nearest_neighbors( + ticker=self.ticker, + data=self.data[self.target], + n_neighbors=ns_parser.n_neighbors, + n_input_days=ns_parser.n_inputs, + n_predict_days=ns_parser.n_days, + test_size=ns_parser.valid_split, + end_date=ns_parser.s_end_date, + no_shuffle=ns_parser.no_shuffle, + ) + + @log_start_end(log=logger) + def call_regression(self, other_args: List[str]): + """Process linear command""" + parser = argparse.ArgumentParser( + add_help=False, + prog="regression", + description=""" + Regression attempts to model the relationship between + two variables by fitting a linear/quadratic/cubic/other equation to + observed data. One variable is considered to be an explanatory variable, + and the other is considered to be a dependent variable. + """, + ) + + parser.add_argument( + "-i", + "--input", + action="store", + dest="n_inputs", + type=check_positive, + default=40, + help="number of days to use for prediction.", + ) + parser.add_argument( + "-d", + "--days", + action="store", + dest="n_days", + type=check_positive, + default=5, + help="prediction days.", + ) + parser.add_argument( + "-j", + "--jumps", + action="store", + dest="n_jumps", + type=check_positive, + default=1, + help="number of jumps in training data.", + ) + parser.add_argument( + "-e", + "--end", + action="store", + type=valid_date, + dest="s_end_date", + default=None, + help="The end date (format YYYY-MM-DD) to select - Backtesting", + ) + parser.add_argument( + "-p", + "--polynomial", + action="store", + dest="n_polynomial", + type=check_positive, + default=1, + help="polynomial associated with regression.", + ) + if ( + other_args + and "-h" not in other_args + and ("-p" not in other_args or "--polynomial" not in other_args) + ): + other_args.insert(0, "-p") + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED + ) + if ns_parser: + # BACKTESTING CHECK + if ns_parser.s_end_date: + if ns_parser.s_end_date < self.data.index[0]: + console.print( + "Backtesting not allowed, since End Date is older than Start Date of historical data\n" + ) + + if ( + ns_parser.s_end_date + < get_next_stock_market_days( + last_stock_day=self.data.index[0], + n_next_days=5 + ns_parser.n_days, + )[-1] + ): + console.print( + "Backtesting not allowed, since End Date is too close to Start Date to train model\n" + ) + + regression_view.display_regression( + dataset=self.ticker, + values=self.data[self.target], + poly_order=ns_parser.n_polynomial, + n_input=ns_parser.n_inputs, + n_predict=ns_parser.n_days, + n_jumps=ns_parser.n_jumps, + s_end_date=ns_parser.s_end_date, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_arima(self, other_args: List[str]): + """Process arima command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="arima", + description=""" + In statistics and econometrics, and in particular in time series analysis, an + autoregressive integrated moving average (ARIMA) model is a generalization of an + autoregressive moving average (ARMA) model. Both of these models are fitted to time + series data either to better understand the data or to predict future points in the + series (forecasting). ARIMA(p,d,q) where parameters p, d, and q are non-negative + integers, p is the order (number of time lags) of the autoregressive model, d is the + degree of differencing (the number of times the data have had past values subtracted), + and q is the order of the moving-average model. + """, + ) + parser.add_argument( + "-d", + "--days", + action="store", + dest="n_days", + type=check_positive, + default=5, + help="prediction days.", + ) + parser.add_argument( + "-i", + "--ic", + action="store", + dest="s_ic", + type=str, + default="aic", + choices=arima_model.ICS, + help="information criteria.", + ) + parser.add_argument( + "-s", + "--seasonal", + action="store_true", + default=False, + dest="b_seasonal", + help="Use weekly seasonal data.", + ) + parser.add_argument( + "-o", + "--order", + action="store", + dest="s_order", + default="", + type=str, + help="arima model order (p,d,q) in format: p,d,q.", + ) + parser.add_argument( + "-r", + "--results", + action="store_true", + dest="b_results", + default=False, + help="results about ARIMA summary flag.", + ) + parser.add_argument( + "-e", + "--end", + action="store", + type=valid_date, + dest="s_end_date", + default=None, + help="The end date (format YYYY-MM-DD) to select - Backtesting", + ) + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED + ) + if ns_parser: + # BACKTESTING CHECK + if ns_parser.s_end_date: + + if ns_parser.s_end_date < self.data.index[0]: + console.print( + "Backtesting not allowed, since End Date is older than Start Date of historical data\n" + ) + + if ( + ns_parser.s_end_date + < get_next_stock_market_days( + last_stock_day=self.data.index[0], + n_next_days=5 + ns_parser.n_days, + )[-1] + ): + console.print( + "Backtesting not allowed, since End Date is too close to Start Date to train model\n" + ) + + arima_view.display_arima( + dataset=self.ticker, + values=self.data[self.target], + arima_order=ns_parser.s_order, + n_predict=ns_parser.n_days, + seasonal=ns_parser.b_seasonal, + ic=ns_parser.s_ic, + results=ns_parser.b_results, + s_end_date=ns_parser.s_end_date, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_mlp(self, other_args: List[str]): + """Process mlp command""" + try: + ns_parser = pred_helper.parse_args( + prog="mlp", + description="""Multi-Layered-Perceptron. """, + other_args=other_args, + ) + if ns_parser: + neural_networks_view.display_mlp( + dataset=self.ticker, + data=self.data[self.target], + n_input_days=ns_parser.n_inputs, + n_predict_days=ns_parser.n_days, + learning_rate=ns_parser.lr, + epochs=ns_parser.n_epochs, + batch_size=ns_parser.n_batch_size, + test_size=ns_parser.valid_split, + n_loops=ns_parser.n_loops, + no_shuffle=ns_parser.no_shuffle, + ) + except Exception as e: + logger.exception(str(e)) + console.print(e, "\n") + + finally: + pred_helper.restore_env() + + @log_start_end(log=logger) + def call_rnn(self, other_args: List[str]): + """Process rnn command""" + try: + ns_parser = pred_helper.parse_args( + prog="rnn", + description="""Recurrent Neural Network. """, + other_args=other_args, + ) + if ns_parser: + neural_networks_view.display_rnn( + dataset=self.ticker, + data=self.data[self.target], + n_input_days=ns_parser.n_inputs, + n_predict_days=ns_parser.n_days, + learning_rate=ns_parser.lr, + epochs=ns_parser.n_epochs, + batch_size=ns_parser.n_batch_size, + test_size=ns_parser.valid_split, + n_loops=ns_parser.n_loops, + no_shuffle=ns_parser.no_shuffle, + ) + + except Exception as e: + logger.exception(str(e)) + console.print(e, "\n") + + finally: + pred_helper.restore_env() + + @log_start_end(log=logger) + def call_lstm(self, other_args: List[str]): + """Process lstm command""" + try: + ns_parser = pred_helper.parse_args( + prog="lstm", + description="""Long-Short Term Memory. """, + other_args=other_args, + ) + if ns_parser: + neural_networks_view.display_lstm( + dataset=self.ticker, + data=self.data[self.target], + n_input_days=ns_parser.n_inputs, + n_predict_days=ns_parser.n_days, + learning_rate=ns_parser.lr, + epochs=ns_parser.n_epochs, + batch_size=ns_parser.n_batch_size, + test_size=ns_parser.valid_split, + n_loops=ns_parser.n_loops, + no_shuffle=ns_parser.no_shuffle, + ) + + except Exception as e: + logger.exception(str(e)) + console.print(e, "\n") + + finally: + pred_helper.restore_env() + + @log_start_end(log=logger) + def call_conv1d(self, other_args: List[str]): + """Process conv1d command""" + try: + ns_parser = pred_helper.parse_args( + prog="conv1d", + description="""1D CNN.""", + other_args=other_args, + ) + if ns_parser: + neural_networks_view.display_conv1d( + dataset=self.ticker, + data=self.data[self.target], + n_input_days=ns_parser.n_inputs, + n_predict_days=ns_parser.n_days, + learning_rate=ns_parser.lr, + epochs=ns_parser.n_epochs, + batch_size=ns_parser.n_batch_size, + test_size=ns_parser.valid_split, + n_loops=ns_parser.n_loops, + no_shuffle=ns_parser.no_shuffle, + ) + + except Exception as e: + logger.exception(str(e)) + console.print(e, "\n") + + finally: + pred_helper.restore_env() + + @log_start_end(log=logger) + def call_mc(self, other_args: List[str]): + """Process mc command""" + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + add_help=False, + prog="mc", + description=""" + Perform Monte Carlo forecasting + """, + ) + parser.add_argument( + "-d", + "--days", + help="Days to predict", + dest="n_days", + type=check_positive, + default=30, + ) + parser.add_argument( + "-n", + "--num", + help="Number of simulations to perform", + dest="n_sims", + default=100, + ) + parser.add_argument( + "--dist", + choices=mc_model.DISTRIBUTIONS, + default="lognormal", + dest="dist", + help="Whether to model returns or log returns", + ) + + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED + ) + if ns_parser: + if self.target != "AdjClose": + console.print("MC Prediction designed for AdjClose prices\n") + + mc_view.display_mc_forecast( + data=self.data[self.target], + n_future=ns_parser.n_days, + n_sims=ns_parser.n_sims, + use_log=ns_parser.dist == "lognormal", + export=ns_parser.export, + ) diff --git a/openbb_terminal/forex/quantitative_analysis/__init__.py b/openbb_terminal/forex/quantitative_analysis/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/openbb_terminal/forex/quantitative_analysis/qa_controller.py b/openbb_terminal/forex/quantitative_analysis/qa_controller.py new file mode 100644 index 000000000000..5ee4cd779a2d --- /dev/null +++ b/openbb_terminal/forex/quantitative_analysis/qa_controller.py @@ -0,0 +1,704 @@ +"""Quantitative Analysis Controller Module""" +__docformat__ = "numpy" + +import argparse +import logging +from typing import List + +import numpy as np +import pandas as pd +from prompt_toolkit.completion import NestedCompleter + +from openbb_terminal import feature_flags as obbff +from openbb_terminal.common.quantitative_analysis import qa_view, rolling_view +from openbb_terminal.decorators import log_start_end +from openbb_terminal.helper_funcs import ( + EXPORT_ONLY_FIGURES_ALLOWED, + EXPORT_ONLY_RAW_DATA_ALLOWED, + check_positive, + check_proportion_range, + parse_known_args_and_warn, + check_list_dates, +) +from openbb_terminal.menu import session +from openbb_terminal.parent_classes import CryptoBaseController +from openbb_terminal.rich_config import console + +logger = logging.getLogger(__name__) + + +class QaController(CryptoBaseController): + """Quantitative Analysis Controller class""" + + CHOICES_COMMANDS = [ + "pick", + "raw", + "summary", + "line", + "hist", + "cdf", + "bw", + "rolling", + "decompose", + "cusum", + "acf", + "spread", + "quantile", + "skew", + "kurtosis", + "normality", + "qqplot", + "unitroot", + "goodness", + "unitroot", + ] + + PATH = "/forex/qa/" + + def __init__( + self, + from_symbol: str, + to_symbol: str, + data: pd.DataFrame, + queue: List[str] = None, + ): + """Constructor""" + super().__init__(queue) + + data["Returns"] = data["Close"].pct_change() + data["LogRet"] = np.log(data["Close"]) - np.log(data["Close"].shift(1)) + data = data.dropna() + + self.data = data + self.from_symbol = from_symbol + self.to_symbol = to_symbol + self.ticker = f"{from_symbol}/{to_symbol}" + self.target = "Close" + + if session and obbff.USE_PROMPT_TOOLKIT: + choices: dict = {c: {} for c in self.controller_choices} + choices["pick"] = {c: None for c in list(data.columns)} + self.completer = NestedCompleter.from_nested_dict(choices) + + def print_help(self): + """Print help""" + help_text = f"""[cmds] + pick pick target column for analysis[/cmds] + +[param]Pair: [/param]{self.ticker} +[param]Target Column: [/param]{self.target} +[cmds] +[info]Statistics:[/info] + summary brief summary statistics of loaded pair. + normality normality statistics and tests + unitroot unit root test for stationarity (ADF, KPSS) +[info]Plots:[/info] + line line plot of selected target + hist histogram with density plot + cdf cumulative distribution function + bw box and whisker plot + acf (partial) auto-correlation function differentials of prices + qqplot residuals against standard normal curve +[info]Rolling Metrics:[/info] + rolling rolling mean and std deviation of prices + spread rolling variance and std deviation of prices + quantile rolling median and quantile of prices + skew rolling skewness of distribution of prices + kurtosis rolling kurtosis of distribution of prices +[info]Risk:[/info] + var display value at risk + es display expected shortfall +[info]Other:[/info] + raw print raw data + decompose decomposition in cyclic-trend, season, and residuals of prices + cusum detects abrupt changes using cumulative sum algorithm of prices[/cmds] + """ + console.print(text=help_text, menu="Forex - Quantitative Analysis") + + def custom_reset(self): + """Class specific component of reset command""" + if self.ticker: + return [ + "forex", + f"from {self.from_symbol}", + f"to {self.to_symbol}", + "load", + "qa", + ] + return [] + + @log_start_end(log=logger) + def call_pick(self, other_args: List[str]): + """Process pick command""" + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + add_help=False, + prog="pick", + description=""" + Change target variable + """, + ) + parser.add_argument( + "-t", + "--target", + dest="target", + choices=list(self.data.columns), + help="Select variable to analyze", + ) + if other_args and "-t" not in other_args and "-h" not in other_args: + other_args.insert(0, "-t") + + ns_parser = parse_known_args_and_warn(parser, other_args) + if ns_parser: + self.target = ns_parser.target + console.print("") + + @log_start_end(log=logger) + def call_raw(self, other_args: List[str]): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + add_help=False, + prog="raw", + description=""" + Print raw data to console + """, + ) + parser.add_argument( + "-l", + "--limit", + help="Number to show", + type=check_positive, + default=20, + dest="limit", + ) + parser.add_argument( + "-d", + "--descend", + action="store_true", + default=False, + dest="descend", + help="Sort in descending order", + ) + + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED + ) + if ns_parser: + qa_view.display_raw( + self.data[self.target], + num=ns_parser.limit, + sort="", + des=ns_parser.descend, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_summary(self, other_args: List[str]): + """Process summary command""" + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + add_help=False, + prog="summary", + description=""" + Summary statistics + """, + ) + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED + ) + if ns_parser: + qa_view.display_summary(df=self.data, export=ns_parser.export) + + @log_start_end(log=logger) + def call_line(self, other_args: List[str]): + """Process line command""" + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + add_help=False, + prog="line", + description="Show line plot of selected data and allow to draw lines or highlight specific datetimes.", + ) + parser.add_argument( + "--log", + help="Plot with y on log scale", + dest="log", + action="store_true", + default=False, + ) + parser.add_argument( + "-d", + "--draw", + help="Draw lines and annotate on the plot", + dest="draw", + action="store_true", + default=False, + ) + parser.add_argument( + "--ml", + help="Draw vertical line markers to highlight certain events", + dest="ml", + type=check_list_dates, + default="", + ) + parser.add_argument( + "--ms", + help="Draw scatter markers to highlight certain events", + dest="ms", + type=check_list_dates, + default="", + ) + + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED + ) + if ns_parser: + qa_view.display_line( + self.data[self.target], + title=f"{self.ticker} {self.target}", + log_y=ns_parser.log, + draw=ns_parser.draw, + markers_lines=ns_parser.ml, + markers_scatter=ns_parser.ms, + ) + + @log_start_end(log=logger) + def call_hist(self, other_args: List[str]): + """Process hist command""" + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + add_help=False, + prog="hist", + description=""" + Histogram with density and rug + """, + ) + parser.add_argument( + "-b", "--bins", type=check_positive, default=15, dest="n_bins" + ) + ns_parser = parse_known_args_and_warn(parser, other_args) + if ns_parser: + qa_view.display_hist( + name=self.ticker, + df=self.data, + target=self.target, + bins=ns_parser.n_bins, + ) + + @log_start_end(log=logger) + def call_cdf(self, other_args: List[str]): + """Process cdf command""" + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + add_help=False, + prog="cdf", + description=""" + Cumulative distribution function + """, + ) + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED + ) + if ns_parser: + qa_view.display_cdf( + name=self.ticker, + df=self.data, + target=self.target, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_bw(self, other_args: List[str]): + """Process bwy command""" + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + add_help=False, + prog="bw", + description=""" + Box and Whisker plot + """, + ) + parser.add_argument( + "-y", + "--yearly", + action="store_true", + default=False, + dest="year", + help="Flag to show yearly bw plot", + ) + ns_parser = parse_known_args_and_warn(parser, other_args) + if ns_parser: + qa_view.display_bw( + name=self.ticker, + df=self.data, + target=self.target, + yearly=ns_parser.year, + ) + + @log_start_end(log=logger) + def call_decompose(self, other_args: List[str]): + """Process decompose command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="decompose", + description=""" + Decompose time series as: + - Additive Time Series = Level + CyclicTrend + Residual + Seasonality + - Multiplicative Time Series = Level * CyclicTrend * Residual * Seasonality + """, + ) + parser.add_argument( + "-m", + "--multiplicative", + action="store_true", + default=False, + dest="multiplicative", + help="decompose using multiplicative model instead of additive", + ) + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED + ) + if ns_parser: + qa_view.display_seasonal( + name=self.ticker, + df=self.data, + target=self.target, + multiplicative=ns_parser.multiplicative, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_cusum(self, other_args: List[str]): + """Process cusum command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="cusum", + description=""" + Cumulative sum algorithm (CUSUM) to detect abrupt changes in data + """, + ) + parser.add_argument( + "-t", + "--threshold", + dest="threshold", + type=float, + default=( + max(self.data[self.target].values) - min(self.data[self.target].values) + ) + / 40, + help="threshold", + ) + parser.add_argument( + "-d", + "--drift", + dest="drift", + type=float, + default=( + max(self.data[self.target].values) - min(self.data[self.target].values) + ) + / 80, + help="drift", + ) + ns_parser = parse_known_args_and_warn(parser, other_args) + if ns_parser: + qa_view.display_cusum( + df=self.data, + target=self.target, + threshold=ns_parser.threshold, + drift=ns_parser.drift, + ) + + @log_start_end(log=logger) + def call_acf(self, other_args: List[str]): + """Process acf command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="acf", + description=""" + Auto-Correlation and Partial Auto-Correlation Functions for diff and diff diff forex data + """, + ) + parser.add_argument( + "-l", + "--lags", + dest="lags", + type=check_positive, + default=15, + help="maximum lags to display in plots", + ) + ns_parser = parse_known_args_and_warn(parser, other_args) + if ns_parser: + if self.target != "Close": + console.print( + "Target not Close. For best results, use `pick Close` first." + ) + + qa_view.display_acf( + name=self.ticker, + df=self.data, + target=self.target, + lags=ns_parser.lags, + ) + + @log_start_end(log=logger) + def call_rolling(self, other_args: List[str]): + """Process rolling command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="rolling", + description=""" + Rolling mean and std deviation + """, + ) + parser.add_argument( + "-w", + "--window", + action="store", + dest="n_window", + type=check_positive, + default=14, + help="Window length", + ) + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED + ) + if ns_parser: + rolling_view.display_mean_std( + name=self.ticker, + df=self.data, + target=self.target, + window=ns_parser.n_window, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_spread(self, other_args: List[str]): + """Process spread command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="spread", + description="""Shows rolling spread measurement + """, + ) + parser.add_argument( + "-w", + "--window", + action="store", + dest="n_window", + type=check_positive, + default=14, + help="Window length", + ) + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED + ) + if ns_parser: + rolling_view.display_spread( + name=self.ticker, + df=self.data, + target=self.target, + window=ns_parser.n_window, + export=ns_parser.export, + ) + + @log_start_end(log=logger) + def call_quantile(self, other_args: List[str]): + """Process quantile command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="quantile", + description=""" + The quantiles are values which divide the distribution such that + there is a given proportion of observations below the quantile. + For example, the median is a quantile. The median is the central + value of the distribution, such that half the points are less than + or equal to it and half are greater than or equal to it. + + By default, q is set at 0.5, which effectively is median. Change q to + get the desired quantile (0 ta technical analysis for loaded coin, e.g.: ema, macd, rsi, adx, bbands, obv +> qa quantitative analysis, e.g.: decompose, cusum, residuals analysis +> pred prediction techniques e.g.: regression, arima, rnn, lstm, conv1d, monte carlo [/dim] Forex brokerages: > oanda Oanda menu From 2a2866c8edd3b63ca49318a218b6938eac610406 Mon Sep 17 00:00:00 2001 From: jose-donato Date: Wed, 6 Apr 2022 23:19:07 +0100 Subject: [PATCH 2/5] updated test help --- .../txt/test_crypto_controller/test_print_help.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/openbb_terminal/cryptocurrency/txt/test_crypto_controller/test_print_help.txt b/tests/openbb_terminal/cryptocurrency/txt/test_crypto_controller/test_print_help.txt index af4bf38ad742..b88f693bdd9d 100644 --- a/tests/openbb_terminal/cryptocurrency/txt/test_crypto_controller/test_print_help.txt +++ b/tests/openbb_terminal/cryptocurrency/txt/test_crypto_controller/test_print_help.txt @@ -16,7 +16,7 @@ Source: > nft non-fungible tokens, e.g.: today drops[unvl] > dd due-diligence for loaded coin, e.g.: coin information, social media, market stats > ta technical analysis for loaded coin, e.g.: ema, macd, rsi, adx, bbands, obv -> pred prediction techniques e.g.: regression, arima, rnn, lstm, conv1d, monte carlo -> qa quantitative analysis, e.g.: decompose, cusum, residuals analysis +> pred prediction techniques, e.g.: regression, arima, rnn, lstm, conv1d, monte carlo +> qa quantitative analysis e.g.: decompose, cusum, residuals analysis [/unvl] From 4d4a8ad1cf730508c68d023a5c169b96d1eec991 Mon Sep 17 00:00:00 2001 From: Theodore Aptekarev Date: Fri, 8 Apr 2022 19:45:29 +0300 Subject: [PATCH 3/5] Add forex/qa notebooks api wrapper --- .../forex/quantitative_analysis/qa_api.py | 57 +++++++++++++++++++ 1 file changed, 57 insertions(+) create mode 100644 openbb_terminal/forex/quantitative_analysis/qa_api.py diff --git a/openbb_terminal/forex/quantitative_analysis/qa_api.py b/openbb_terminal/forex/quantitative_analysis/qa_api.py new file mode 100644 index 000000000000..760d0d6e732c --- /dev/null +++ b/openbb_terminal/forex/quantitative_analysis/qa_api.py @@ -0,0 +1,57 @@ +"""Quantitative Analysis API.""" +import os +from openbb_terminal.helper_classes import ModelsNamespace as _models + +# flake8: noqa +# pylint: disable=unused-import + +# Menu commands +from openbb_terminal.common.quantitative_analysis.qa_view import display_raw as raw +from openbb_terminal.common.quantitative_analysis.qa_view import ( + display_summary as summary, +) +from openbb_terminal.common.quantitative_analysis.qa_view import display_line as line +from openbb_terminal.common.quantitative_analysis.qa_view import display_hist as hist +from openbb_terminal.common.quantitative_analysis.qa_view import display_cdf as cdf +from openbb_terminal.common.quantitative_analysis.qa_view import display_bw as bw +from openbb_terminal.common.quantitative_analysis.qa_view import ( + display_seasonal as decompose, +) +from openbb_terminal.common.quantitative_analysis.qa_view import ( + display_cusum as cusum, +) +from openbb_terminal.common.quantitative_analysis.qa_view import display_acf as acf +from openbb_terminal.common.quantitative_analysis.rolling_view import ( + display_mean_std as rolling, +) +from openbb_terminal.common.quantitative_analysis.rolling_view import ( + display_spread as spread, +) +from openbb_terminal.common.quantitative_analysis.rolling_view import ( + display_quantile as quantile, +) +from openbb_terminal.common.quantitative_analysis.rolling_view import ( + display_skew as skew, +) +from openbb_terminal.common.quantitative_analysis.rolling_view import ( + display_kurtosis as kurtosis, +) +from openbb_terminal.common.quantitative_analysis.qa_view import ( + display_normality as normality, +) +from openbb_terminal.common.quantitative_analysis.qa_view import ( + display_qqplot as qqplot, +) +from openbb_terminal.common.quantitative_analysis.qa_view import ( + display_unitroot as unitroot, +) + +# Models +# NOTE: The raw function is used here to point to the commons path where all the +# qa models are expected to live +models = _models( + [ + os.path.abspath(os.path.dirname(raw.__code__.co_filename)), + os.path.abspath(os.path.dirname(__file__)), + ] +) From d1244d5567052e6704c97bab85b63639894501c9 Mon Sep 17 00:00:00 2001 From: Theodore Aptekarev Date: Fri, 8 Apr 2022 19:46:57 +0300 Subject: [PATCH 4/5] Add forex/qa tests --- .../forex/quantitative_analysis/__init__.py | 0 .../test_qa_controller/test_call_load.yaml | 16326 ++++++++++++++++ .../quantitative_analysis/test_qa_api.py | 11 + .../test_qa_controller.py | 551 + .../test_qa_controller/test_print_help.txt | 31 + 5 files changed, 16919 insertions(+) create mode 100644 tests/openbb_terminal/forex/quantitative_analysis/__init__.py create mode 100644 tests/openbb_terminal/forex/quantitative_analysis/cassettes/test_qa_controller/test_call_load.yaml create mode 100644 tests/openbb_terminal/forex/quantitative_analysis/test_qa_api.py create mode 100644 tests/openbb_terminal/forex/quantitative_analysis/test_qa_controller.py create mode 100644 tests/openbb_terminal/forex/quantitative_analysis/txt/test_qa_controller/test_print_help.txt diff --git a/tests/openbb_terminal/forex/quantitative_analysis/__init__.py b/tests/openbb_terminal/forex/quantitative_analysis/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/tests/openbb_terminal/forex/quantitative_analysis/cassettes/test_qa_controller/test_call_load.yaml b/tests/openbb_terminal/forex/quantitative_analysis/cassettes/test_qa_controller/test_call_load.yaml new file mode 100644 index 000000000000..ca2664ac530a --- /dev/null +++ b/tests/openbb_terminal/forex/quantitative_analysis/cassettes/test_qa_controller/test_call_load.yaml @@ -0,0 +1,16326 @@ +interactions: +- request: + body: null + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + method: GET + uri: https://api.coinpaprika.com/v1/coins/ + response: + body: + string: !!binary | + H4sIAAAAAAAA/6S92XLjOPMv+CqKnptzIv7Z0bXa/u602VZbW0nyUjUXEyAJSWiRAAsEtXhinmce + ZF5sgouEBMgvopW+MFRIuDtNCkjk8svM//P//kNEf/znj8CEEAgTKiH/+J8/JEv4H//5o3chZKck + UHFBWvX/+J8/NJO7P/7z6X/+ENn/Jfnhj/+sWZzxcspCI/b8j/8YnfP/+cOc0uJ/VP5f/p//qXlx + swVutlzzPLHMhpZy4TZcPV64fSZyy7PIgCn5WWar8/zC6nk5WF14ffnXvIzaccQskAEEQjIZcvDf + ZUnt9P0XOu1d2H6lP2IIeRZ5LJ+Xgwa75+XAfoHfqI+55UfY8qNl9Th8w1yqacXkO5VJpmLIVMwk + s3yW5/mF1XI2vrC6obI66hSOOrV83hZzzKSaVkxuiV8SixiETEdMKsunfyFceHUH3QuvOyKvOJcM + DNea4S1fTS+Mxs9Ty+nTX9TH2rMjsD2LmQy33LLrIpJ9tpeu3RifqPIjUhsOxeBu94GlXBgOZg9D + y5AqQoLibJ2PdZ5FzVP9vBw4h7qa12zJ0iRSBlIV71ikjOU5txT0nFZ4faKLEVNtGucRy33jPd/z + 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Content-Type: + - application/json; charset=UTF-8 + Date: + - Fri, 08 Apr 2022 16:40:33 GMT + Expect-CT: + - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" + Last-Modified: + - Fri, 08 Apr 2022 16:39:34 GMT + Server: + - cloudflare + Transfer-Encoding: + - chunked + Vary: + - Accept-Encoding,Origin + alt-svc: + - h3=":443"; ma=86400, h3-29=":443"; ma=86400 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/openbb_terminal/forex/quantitative_analysis/test_qa_api.py b/tests/openbb_terminal/forex/quantitative_analysis/test_qa_api.py new file mode 100644 index 000000000000..8f93a7b71935 --- /dev/null +++ b/tests/openbb_terminal/forex/quantitative_analysis/test_qa_api.py @@ -0,0 +1,11 @@ +# IMPORTATION STANDARD + +# IMPORTATION THIRDPARTY + +# IMPORTATION INTERNAL +from openbb_terminal.helper_classes import ModelsNamespace as _models +from openbb_terminal.forex.quantitative_analysis import qa_api + + +def test_models(): + assert isinstance(qa_api.models, _models) diff --git a/tests/openbb_terminal/forex/quantitative_analysis/test_qa_controller.py b/tests/openbb_terminal/forex/quantitative_analysis/test_qa_controller.py new file mode 100644 index 000000000000..e022c5a81f40 --- /dev/null +++ b/tests/openbb_terminal/forex/quantitative_analysis/test_qa_controller.py @@ -0,0 +1,551 @@ +# IMPORTATION STANDARD +import os + +# IMPORTATION THIRDPARTY +import pandas as pd +import pytest + +# IMPORTATION INTERNAL +from openbb_terminal.forex.quantitative_analysis import qa_controller + + +DF_PAIR = pd.DataFrame.from_dict( + data={ + pd.Timestamp("2020-11-30 00:00:00"): { + "Open": 75.69999694824219, + "High": 76.08999633789062, + "Low": 75.41999816894531, + "Close": 75.75, + "Adj Close": 71.90919494628906, + "Volume": 5539100, + "date_id": 1, + "OC_High": 75.75, + "OC_Low": 75.69999694824219, + }, + pd.Timestamp("2020-12-01 00:00:00"): { + "Open": 76.0199966430664, + "High": 77.12999725341797, + "Low": 75.69000244140625, + "Close": 77.02999877929688, + "Adj Close": 73.1242904663086, + "Volume": 6791700, + "date_id": 2, + "OC_High": 77.02999877929688, + "OC_Low": 76.0199966430664, + }, + }, + orient="index", +) +EMPTY_DF = pd.DataFrame() +QA_CONTROLLER = qa_controller.QaController( + from_symbol="MOCK_TICKER", + to_symbol="MOCK_TICKER", + data=DF_PAIR.copy(), +) + + +@pytest.fixture(scope="module") +def vcr_config(): + return { + "filter_headers": [("User-Agent", None)], + "filter_query_parameters": [ + ("period1", "MOCK_PERIOD_1"), + ("period2", "MOCK_PERIOD_2"), + ("date", "MOCK_DATE"), + ], + } + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.parametrize( + "queue, expected", + [ + (["load", "help"], []), + (["quit", "help"], ["help"]), + ], +) +def test_menu_with_queue(expected, mocker, queue): + mocker.patch( + target=( + "openbb_terminal.forex.quantitative_analysis.qa_controller." + "QaController.switch" + ), + return_value=["quit"], + ) + result_menu = qa_controller.QaController( + from_symbol="MOCK_TICKER", + to_symbol="MOCK_TICKER", + data=DF_PAIR.copy(), + queue=queue, + ).menu() + + assert result_menu == expected + + +@pytest.mark.vcr(record_mode="none") +def test_menu_without_queue_completion(mocker): + # ENABLE AUTO-COMPLETION : HELPER_FUNCS.MENU + mocker.patch( + target="openbb_terminal.feature_flags.USE_PROMPT_TOOLKIT", + new=True, + ) + mocker.patch( + target="openbb_terminal.parent_classes.session", + ) + mocker.patch( + target="openbb_terminal.parent_classes.session.prompt", + return_value="quit", + ) + + # DISABLE AUTO-COMPLETION : CONTROLLER.COMPLETER + mocker.patch.object( + target=qa_controller.obbff, + attribute="USE_PROMPT_TOOLKIT", + new=True, + ) + mocker.patch( + target="openbb_terminal.forex.quantitative_analysis.qa_controller.session", + ) + mocker.patch( + target="openbb_terminal.forex.quantitative_analysis.qa_controller.session.prompt", + return_value="quit", + ) + + result_menu = qa_controller.QaController( + from_symbol="MOCK_TICKER", + to_symbol="MOCK_TICKER", + data=DF_PAIR.copy(), + queue=None, + ).menu() + + assert result_menu == [] + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.parametrize( + "mock_input", + ["help", "homee help", "home help", "mock"], +) +def test_menu_without_queue_sys_exit(mock_input, mocker): + # DISABLE AUTO-COMPLETION + mocker.patch.object( + target=qa_controller.obbff, + attribute="USE_PROMPT_TOOLKIT", + new=False, + ) + mocker.patch( + target="openbb_terminal.forex.quantitative_analysis.qa_controller.session", + return_value=None, + ) + + # MOCK USER INPUT + mocker.patch("builtins.input", return_value=mock_input) + + # MOCK SWITCH + class SystemExitSideEffect: + def __init__(self): + self.first_call = True + + def __call__(self, *args, **kwargs): + if self.first_call: + self.first_call = False + raise SystemExit() + return ["quit"] + + mock_switch = mocker.Mock(side_effect=SystemExitSideEffect()) + mocker.patch( + target=( + "openbb_terminal.forex.quantitative_analysis.qa_controller." + "QaController.switch" + ), + new=mock_switch, + ) + + result_menu = qa_controller.QaController( + from_symbol="MOCK_TICKER", + to_symbol="MOCK_TICKER", + data=DF_PAIR.copy(), + queue=None, + ).menu() + + assert result_menu == [] + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.record_stdout +def test_print_help(): + controller = qa_controller.QaController( + from_symbol="MOCK_TICKER", + to_symbol="MOCK_TICKER", + data=DF_PAIR.copy(), + ) + controller.print_help() + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.parametrize( + "an_input, expected_queue", + [ + ("", []), + ("/help", ["home", "help"]), + ("help/help", ["help", "help"]), + ("q", ["quit"]), + ("h", []), + ( + "r", + [ + "quit", + "quit", + "reset", + "forex", + "from MOCK_TICKER", + "to MOCK_TICKER", + "load", + "qa", + ], + ), + ], +) +def test_switch(an_input, expected_queue): + controller = qa_controller.QaController( + from_symbol="MOCK_TICKER", + to_symbol="MOCK_TICKER", + data=DF_PAIR.copy(), + queue=None, + ) + queue = controller.switch(an_input=an_input) + + assert queue == expected_queue + + +@pytest.mark.vcr(record_mode="none") +def test_call_cls(mocker): + mocker.patch("os.system") + controller = qa_controller.QaController( + from_symbol="MOCK_TICKER", + to_symbol="MOCK_TICKER", + data=DF_PAIR.copy(), + ) + controller.call_cls([]) + + assert controller.queue == [] + os.system.assert_called_once_with("cls||clear") + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.parametrize( + "func, queue, expected_queue", + [ + ( + "call_exit", + [], + [ + "quit", + "quit", + "quit", + ], + ), + ("call_exit", ["help"], ["quit", "quit", "quit", "help"]), + ("call_home", [], ["quit", "quit"]), + ("call_help", [], []), + ("call_quit", [], ["quit"]), + ("call_quit", ["help"], ["quit", "help"]), + ( + "call_reset", + [], + [ + "quit", + "quit", + "reset", + "forex", + "from MOCK_TICKER", + "to MOCK_TICKER", + "load", + "qa", + ], + ), + ( + "call_reset", + ["help"], + [ + "quit", + "quit", + "reset", + "forex", + "from MOCK_TICKER", + "to MOCK_TICKER", + "load", + "qa", + "help", + ], + ), + ], +) +def test_call_func_expect_queue(expected_queue, queue, func): + controller = qa_controller.QaController( + from_symbol="MOCK_TICKER", + to_symbol="MOCK_TICKER", + data=DF_PAIR.copy(), + queue=queue, + ) + result = getattr(controller, func)([]) + + assert result is None + assert controller.queue == expected_queue + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.parametrize( + "tested_func, other_args, mocked_func, called_args, called_kwargs", + [ + ( + "call_pick", + [QA_CONTROLLER.target], + "", + [], + dict(), + ), + ( + "call_raw", + ["--limit=1", "--descend", "--export=csv"], + "qa_view.display_raw", + [QA_CONTROLLER.data[QA_CONTROLLER.target]], + dict( + num=1, + sort="", + des=True, + export="csv", + ), + ), + ( + "call_summary", + ["--export=csv"], + "qa_view.display_summary", + [], + dict( + df=QA_CONTROLLER.data, + export="csv", + ), + ), + ( + "call_hist", + ["--bins=1"], + "qa_view.display_hist", + [], + dict( + name=QA_CONTROLLER.ticker, + df=QA_CONTROLLER.data, + target=QA_CONTROLLER.target, + bins=1, + ), + ), + ( + "call_cdf", + ["--export=csv"], + "qa_view.display_cdf", + [], + dict( + name=QA_CONTROLLER.ticker, + df=QA_CONTROLLER.data, + target=QA_CONTROLLER.target, + export="csv", + ), + ), + ( + "call_bw", + ["--yearly"], + "qa_view.display_bw", + [], + dict( + name=QA_CONTROLLER.ticker, + df=QA_CONTROLLER.data, + target=QA_CONTROLLER.target, + yearly=True, + ), + ), + ( + "call_rolling", + ["--window=1", "--export=csv"], + "rolling_view.display_mean_std", + [], + dict( + name=QA_CONTROLLER.ticker, + df=QA_CONTROLLER.data, + target=QA_CONTROLLER.target, + window=1, + export="csv", + ), + ), + ( + "call_decompose", + ["--multiplicative", "--export=csv"], + "qa_view.display_seasonal", + [], + dict( + name=QA_CONTROLLER.ticker, + df=QA_CONTROLLER.data, + target=QA_CONTROLLER.target, + multiplicative=True, + export="csv", + ), + ), + ( + "call_cusum", + ["--threshold=1", "--drift=2"], + "qa_view.display_cusum", + [], + dict( + df=QA_CONTROLLER.data, + target=QA_CONTROLLER.target, + threshold=1, + drift=2, + ), + ), + ( + "call_acf", + ["--lags=1"], + "qa_view.display_acf", + [], + dict( + name=QA_CONTROLLER.ticker, + df=QA_CONTROLLER.data, + target=QA_CONTROLLER.target, + lags=1, + ), + ), + ( + "call_spread", + ["--window=1", "--export=csv"], + "rolling_view.display_spread", + [], + dict( + name=QA_CONTROLLER.ticker, + df=QA_CONTROLLER.data, + target=QA_CONTROLLER.target, + window=1, + export="csv", + ), + ), + ( + "call_quantile", + ["--window=1", "--quantile=0.1", "--export=csv"], + "rolling_view.display_quantile", + [], + dict( + name=QA_CONTROLLER.ticker, + df=QA_CONTROLLER.data, + target=QA_CONTROLLER.target, + window=1, + quantile=0.1, + export="csv", + ), + ), + ( + "call_skew", + ["--window=1", "--export=csv"], + "rolling_view.display_skew", + [], + dict( + name=QA_CONTROLLER.ticker, + df=QA_CONTROLLER.data, + target=QA_CONTROLLER.target, + window=1, + export="csv", + ), + ), + ( + "call_kurtosis", + ["--window=1", "--export=csv"], + "rolling_view.display_kurtosis", + [], + dict( + name=QA_CONTROLLER.ticker, + df=QA_CONTROLLER.data, + target=QA_CONTROLLER.target, + window=1, + export="csv", + ), + ), + ( + "call_normality", + ["--export=csv"], + "qa_view.display_normality", + [], + dict( + df=QA_CONTROLLER.data, + target=QA_CONTROLLER.target, + export="csv", + ), + ), + ( + "call_qqplot", + [], + "qa_view.display_qqplot", + [], + dict( + name=QA_CONTROLLER.ticker, + df=QA_CONTROLLER.data, + target=QA_CONTROLLER.target, + ), + ), + ( + "call_unitroot", + ["--fuller_reg=ctt", "--kps_reg=ct", "--export=csv"], + "qa_view.display_unitroot", + [], + dict( + df=QA_CONTROLLER.data, + target=QA_CONTROLLER.target, + fuller_reg="ctt", + kpss_reg="ct", + export="csv", + ), + ), + ], +) +def test_call_func( + tested_func, mocked_func, other_args, called_args, called_kwargs, mocker +): + if mocked_func: + mock = mocker.Mock() + mocker.patch( + "openbb_terminal.forex.quantitative_analysis.qa_controller." + mocked_func, + new=mock, + ) + + getattr(QA_CONTROLLER, tested_func)(other_args=other_args) + + if called_args or called_kwargs: + mock.assert_called_once_with(*called_args, **called_kwargs) + else: + mock.assert_called_once() + else: + getattr(QA_CONTROLLER, tested_func)(other_args=other_args) + + +@pytest.mark.vcr(record_mode="none") +@pytest.mark.parametrize( + "from_symbol, to_symbol, expected", + [ + (None, None, ["forex", "from None", "to None", "load", "qa"]), + ("MOCK_FROM", None, ["forex", "from MOCK_FROM", "to None", "load", "qa"]), + (None, "MOCK_TO", ["forex", "from None", "to MOCK_TO", "load", "qa"]), + ( + "MOCK_FROM", + "MOCK_TO", + ["forex", "from MOCK_FROM", "to MOCK_TO", "load", "qa"], + ), + ], +) +def test_custom_reset(expected, from_symbol, to_symbol): + controller = qa_controller.QaController( + from_symbol=None, + to_symbol=None, + data=DF_PAIR.copy(), + ) + controller.from_symbol = from_symbol + controller.to_symbol = to_symbol + + result = controller.custom_reset() + + assert result == expected diff --git a/tests/openbb_terminal/forex/quantitative_analysis/txt/test_qa_controller/test_print_help.txt b/tests/openbb_terminal/forex/quantitative_analysis/txt/test_qa_controller/test_print_help.txt new file mode 100644 index 000000000000..de2b880a7841 --- /dev/null +++ b/tests/openbb_terminal/forex/quantitative_analysis/txt/test_qa_controller/test_print_help.txt @@ -0,0 +1,31 @@ + + pick pick target column for analysis + +Pair: MOCK_TICKER/MOCK_TICKER +Target Column: Close + +Statistics: + summary brief summary statistics of loaded pair. + normality normality statistics and tests + unitroot unit root test for stationarity (ADF, KPSS) +Plots: + line line plot of selected target + hist histogram with density plot + cdf cumulative distribution function + bw box and whisker plot + acf (partial) auto-correlation function differentials of prices + qqplot residuals against standard normal curve +Rolling Metrics: + rolling rolling mean and std deviation of prices + spread rolling variance and std deviation of prices + quantile rolling median and quantile of prices + skew rolling skewness of distribution of prices + kurtosis rolling kurtosis of distribution of prices +Risk: + var display value at risk + es display expected shortfall +Other: + raw print raw data + decompose decomposition in cyclic-trend, season, and residuals of prices + cusum detects abrupt changes using cumulative sum algorithm of prices + From f26dbea2f210e55e133c084050c08d26e5e7f087 Mon Sep 17 00:00:00 2001 From: Theodore Aptekarev Date: Fri, 8 Apr 2022 19:50:15 +0300 Subject: [PATCH 5/5] Add all menu commands to the integration test script --- scripts/test_forex_qa.openbb | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/scripts/test_forex_qa.openbb b/scripts/test_forex_qa.openbb index 6563f6680421..dd6ff47dba36 100644 --- a/scripts/test_forex_qa.openbb +++ b/scripts/test_forex_qa.openbb @@ -7,4 +7,18 @@ normality unitroot line hist -exit \ No newline at end of file +cdf +bw +acf +qqplot +rolling +spread +quantile +skew +kurtosis +var +es +raw +decompose +cusum +exit