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How to use _greater_than method in pandera

Best Python code snippet using pandera_python

currency_indicator_backtest.py

Source:currency_indicator_backtest.py

1import pandas as pd2import numpy as np3import sys4import matplotlib.pyplot as plt5oandapyV20.endpoints.accounts.AccountInstruments6sys.path.insert(1, '/Users/user/Desktop/diff')7from libraries.oanda import get_candles_bid_close8"""9Backtesting currencies and Indicators and Look for major trends:10        11    1.  Call candles for over multiple years12    2.  Calculate Currencies and Indicator.13    3.  go through Data and Graph.14    4.  Report Findings.15    5.  Save in convenient fashion for reuse - answerting different questions.16    6.  Save pulled candles onto disk so do not need to call Oanda every time.17Questions:18    19    What is volume in candle?20"""21# Candle Parameters22_from = '2019-06-01T00:00:00Z'23_to = '2020-01-01T00:00:00Z'24granularity = 'D'25# Pairs26pairs_index = ['AUD_CAD', 'AUD_CHF', 'AUD_HKD', 'AUD_JPY', 'AUD_NZD',27                'AUD_USD', 'CAD_CHF', 'CAD_HKD', 'CAD_JPY', 'CHF_HKD',28                'CHF_JPY', 'EUR_AUD', 'EUR_CAD', 'EUR_CHF', 'EUR_GBP',29                'EUR_HKD', 'EUR_JPY', 'EUR_NZD', 'EUR_USD', 'GBP_AUD',30                'GBP_CAD', 'GBP_CHF', 'GBP_HKD', 'GBP_JPY', 'GBP_NZD',31                'GBP_USD', 'HKD_JPY', 'NZD_CAD', 'NZD_CHF', 'NZD_HKD',32                'NZD_JPY', 'NZD_USD', 'USD_CAD', 'USD_CHF', 'USD_HKD',33                'USD_JPY']34# Currencies35currencies_index = list(set('_'.join(pairs_index).split('_')))36currencies_index.sort()37# Call Candles and assemble in to appropriate Dataframe38##############################################################################39if True:40    pairs = get_candles_bid_close(pairs_index[0], granularity, _from, _to)41    pairs = pairs.drop('volume', axis=1)42    pairs = pairs.set_index('timestamp', drop=True)43    for pair in pairs_index[1:]:44        df = get_candles_bid_close(pair, granularity, _from, _to)45        df = df.set_index('timestamp', drop=True)46        df = df.drop('volume', axis=1)47        pairs = pairs.join(df, how='inner')48    # Fill Forward missing prices, drop top row if any nans remain49    pairs = pairs.fillna(method='ffill').dropna()50# Calculate Currencies and Indicator for each timestamp51##############################################################################52if True:53    def conversion_dict(pairs, currencies):54        '''55        Calculate currencies based on pairs56        Return Currency dictionary based on full set of instrument prices57        Input: pairs:      dictionary:       currenct value of each pair 58               currencies: dictionary:       values can be zero59        '''60        for currency in currencies.keys():61            # If pair contains currency, add it to the subset, making sure that62            # It is added with the currency as the denominator63            pair_subset = []64            for pair in list(pairs.keys()):65                if currency == pair.split('_')[0]:66                    pair_subset.append(1 / pairs[pair])67                if currency == pair.split('_')[1]:68                    pair_subset.append(    pairs[pair])69            currencies[currency] = 1 / (np.array(pair_subset).sum() + 1 )70        return currencies 71    72    currency_dict = dict(zip(currencies_index, [0] * len(currencies_index)))73    currencies = pd.DataFrame(columns = currencies_index)74    for index, row in pairs.iterrows():75        p = row.to_dict()76        currencies.loc[index] = conversion_dict(p, currency_dict)77        78 79    # Rcalculate Pairs based on currency prices80    calculated = pairs.copy()81    for column in calculated:82        left = column.split('_')[0]83        right = column.split('_')[1]84        calculated[column] = currencies[left] / currencies[right]85        86    # Calculate Difference Indicator   87    differences = calculated - pairs88    89            90# Difference test 191##############################################################################92if False:93    '''94    for a currency:95        Bin amount differences were positive 96        for each bin:97            how many times did it happen?98            what was the percent of time that the next days price was higher?99        100    '''101    102    # Only choose those days where the difference was greater than zero103    104    # This is wrong.105    106    '''107    curr = 'EUR_USD'108    greater_than_0_index = differences[curr].values > 0109    diff_greater = differences[curr].values[greater_than_0_index]110    pair_greater = pairs[curr].values[greater_than_0_index]111    bins = pd.cut(diff_greater, 50, labels=False)112    113    114    _bins = []115    _count = []116    _greater_than = []117    for i in range(bins.max() + 1):118        _bins.append(i)119        _count.append((bins == i).sum())120        # greater_than = (pairs[curr].values[bins == i] < \121        #                 pairs[curr].values[np.roll(bins == i, 1)]).mean()122        greater_than = (pair_greater[bins == i] < \123                        pair_greater[np.roll(bins == i, 1)]).mean()124        _greater_than.append(greater_than)125        126    plt.scatter(_count, _greater_than)127    '''128    129    130    131    132    # WY DOES E_USD RETURN ON 300 RESUTLS  ?? ? ? 133    # ANYWAY - TIRED NOW.134    135    136    137    curr = 'EUR_USD'138    bins = pd.cut(differences[curr], 50, labels=False)139    greater_than_0_index = calculated[curr] > pairs[curr]140    diff_greater = differences[curr].values[greater_than_0_index]141    pair_greater = pairs[curr].values[greater_than_0_index]142    # Simple question - if it was higher, was the next ( what about the last)143    _greater = pairs[curr][greater_than_0_index].values < pairs[curr][np.roll(greater_than_0_index, 1)].values144    print(_greater.mean())145    146    # The answer is no - what about if we bin the sizes147    148    bins = pd.cut(differences[curr][greater_than_0_index].values, 50, labels=False)149    150    151    152    _bins = []153    _count = []154    _greater_than = []155    for i in range(bins.max() + 1):156        _bins.append(i)157        _count.append((bins == i).sum())158        # greater_than = (pairs[curr].values[bins == i] < \159        #                 pairs[curr].values[np.roll(bins == i, 1)]).mean()160        greater_than = (pair_greater[bins == i] < \161                        pair_greater[np.roll(bins == i, 1)]).mean()162        _greater_than.append(greater_than)163        164    plt.scatter(_count, _greater_than)165    ...

whole_currnecy_universe_test.py

Source:whole_currnecy_universe_test.py

1import pandas as pd2import numpy as np3import sys4import matplotlib.pyplot as plt5sys.path.insert(1, '/Users/user/Desktop/diff')6from libraries.oanda import get_candles_bid_close7"""8Backtesting currencies and Indicators and Look for major trends:9        10    1.  Call candles for over multiple years11    2.  Calculate Currencies and Indicator.12    3.  go through Data and Graph.13    4.  Report Findings.14    5.  Save in convenient fashion for reuse - answerting different questions.15    6.  Save pulled candles onto disk so do not need to call Oanda every time.16Questions:17    18    What is volume in candle?19"""20# Candle Parameters21_from = '2019-06-01T00:00:00Z'22_to = '2020-01-01T00:00:00Z'23granularity = 'D'24# Pairs25pairs_index = ['AUD_CAD', 'AUD_CHF', 'AUD_HKD', 'AUD_JPY', 'AUD_NZD',26                'AUD_USD', 'CAD_CHF', 'CAD_HKD', 'CAD_JPY', 'CHF_HKD',27                'CHF_JPY', 'EUR_AUD', 'EUR_CAD', 'EUR_CHF', 'EUR_GBP',28                'EUR_HKD', 'EUR_JPY', 'EUR_NZD', 'EUR_USD', 'GBP_AUD',29                'GBP_CAD', 'GBP_CHF', 'GBP_HKD', 'GBP_JPY', 'GBP_NZD',30                'GBP_USD', 'HKD_JPY', 'NZD_CAD', 'NZD_CHF', 'NZD_HKD',31                'NZD_JPY', 'NZD_USD', 'USD_CAD', 'USD_CHF', 'USD_HKD',32                'USD_JPY']33# Currencies34currencies_index = list(set('_'.join(pairs_index).split('_')))35currencies_index.sort()36# Call Candles and assemble in to appropriate Dataframe37##############################################################################38if True:39    pairs = get_candles_bid_close(pairs_index[0], granularity, _from, _to)40    pairs = pairs.drop('volume', axis=1)41    pairs = pairs.set_index('timestamp', drop=True)42    for pair in pairs_index[1:]:43        df = get_candles_bid_close(pair, granularity, _from, _to)44        df = df.set_index('timestamp', drop=True)45        df = df.drop('volume', axis=1)46        pairs = pairs.join(df, how='inner')47    # Fill Forward missing prices, drop top row if any nans remain48    pairs = pairs.fillna(method='ffill').dropna()49# Calculate Currencies and Indicator for each timestamp50##############################################################################51if True:52    def conversion_dict(pairs, currencies):53        '''54        Calculate currencies based on pairs55        Return Currency dictionary based on full set of instrument prices56        Input: pairs:      dictionary:       currenct value of each pair 57               currencies: dictionary:       values can be zero58        '''59        for currency in currencies.keys():60            # If pair contains currency, add it to the subset, making sure that61            # It is added with the currency as the denominator62            pair_subset = []63            for pair in list(pairs.keys()):64                if currency == pair.split('_')[0]:65                    pair_subset.append(1 / pairs[pair])66                if currency == pair.split('_')[1]:67                    pair_subset.append(    pairs[pair])68            currencies[currency] = 1 / (np.array(pair_subset).sum() + 1 )69        return currencies 70    71    currency_dict = dict(zip(currencies_index, [0] * len(currencies_index)))72    currencies = pd.DataFrame(columns = currencies_index)73    for index, row in pairs.iterrows():74        p = row.to_dict()75        currencies.loc[index] = conversion_dict(p, currency_dict)76        77 78    # Rcalculate Pairs based on currency prices79    calculated = pairs.copy()80    for column in calculated:81        left = column.split('_')[0]82        right = column.split('_')[1]83        calculated[column] = currencies[left] / currencies[right]84        85    # Calculate Difference Indicator   86    differences = calculated - pairs87    88            89# Difference test 190##############################################################################91if False:92    '''93    for a currency:94        Bin amount differences were positive 95        for each bin:96            how many times did it happen?97            what was the percent of time that the next days price was higher?98        99    '''100    101    # Only choose those days where the difference was greater than zero102    103    # This is wrong.104    105    '''106    curr = 'EUR_USD'107    greater_than_0_index = differences[curr].values > 0108    diff_greater = differences[curr].values[greater_than_0_index]109    pair_greater = pairs[curr].values[greater_than_0_index]110    bins = pd.cut(diff_greater, 50, labels=False)111    112    113    _bins = []114    _count = []115    _greater_than = []116    for i in range(bins.max() + 1):117        _bins.append(i)118        _count.append((bins == i).sum())119        # greater_than = (pairs[curr].values[bins == i] < \120        #                 pairs[curr].values[np.roll(bins == i, 1)]).mean()121        greater_than = (pair_greater[bins == i] < \122                        pair_greater[np.roll(bins == i, 1)]).mean()123        _greater_than.append(greater_than)124        125    plt.scatter(_count, _greater_than)126    '''127    128    129    130    131    # WY DOES E_USD RETURN ON 300 RESUTLS  ?? ? ? 132    # ANYWAY - TIRED NOW.133    134    135    136    curr = 'EUR_USD'137    bins = pd.cut(differences[curr], 50, labels=False)138    greater_than_0_index = calculated[curr] > pairs[curr]139    diff_greater = differences[curr].values[greater_than_0_index]140    pair_greater = pairs[curr].values[greater_than_0_index]141    # Simple question - if it was higher, was the next ( what about the last)142    _greater = pairs[curr][greater_than_0_index].values < pairs[curr][np.roll(greater_than_0_index, 1)].values143    print(_greater.mean())144    145    # The answer is no - what about if we bin the sizes146    147    bins = pd.cut(differences[curr][greater_than_0_index].values, 50, labels=False)148    149    150    151    _bins = []152    _count = []153    _greater_than = []154    for i in range(bins.max() + 1):155        _bins.append(i)156        _count.append((bins == i).sum())157        # greater_than = (pairs[curr].values[bins == i] < \158        #                 pairs[curr].values[np.roll(bins == i, 1)]).mean()159        greater_than = (pair_greater[bins == i] < \160                        pair_greater[np.roll(bins == i, 1)]).mean()161        _greater_than.append(greater_than)162        163    plt.scatter(_count, _greater_than)164    ...

ranker.py

Source:ranker.py

1#! /usr/bin/env python2import sys3from functools import cmp_to_key4from itertools import combinations5from pyrsistent import pset, pmap_field, PSet, pset_field, PClass, field6class Ranker(PClass):7    _greater_than = pmap_field(str, PSet)8    _remaining_pairs = pset_field(PSet)9    _all_items = field(initial=pset())10    @classmethod11    def new(cls, items):12        remaining = pset({pset(x) for x in combinations(items, 2)})13        return cls(_remaining_pairs=remaining)14    def is_complete(self):15        return len(self._remaining_pairs) == 016    def add_ranking(self, larger, smaller):17        """ Decide that 'larger' is greater than 'smaller' """18        key = pset([larger, smaller])19        slf = self20        if slf.cmp_items(smaller, larger) > 0:21            raise ValueError(f"{smaller} is already greater than {larger}")22        if key not in slf._remaining_pairs:23            return slf24        # Set larger as being greater than smaller25        slf = slf.set(_remaining_pairs=slf._remaining_pairs.remove(key))26        if larger not in slf._greater_than:27            slf = slf.set(_greater_than=slf._greater_than.set(larger, pset()))28        slf = slf.set(29            _greater_than=slf._greater_than.set(larger, slf._greater_than[larger].add(smaller)),30            _all_items=slf._all_items.add(larger).add(smaller),31        )32        # Set larger as being greater than everything which smaller is greater than33        for item in slf.everying_less_than(larger):34            slf = slf.add_ranking(larger, item)35        return slf36    def sample(self):37        for item in self._remaining_pairs:38            return item39    def everying_less_than(self, item):40        return self._greater_than[item]41    def has_item(self, item):42        return item in self._all_items43    def sorted_items(self, *args, **kwargs):44        return self.sorted(self._all_items, *args, **kwargs)45    def sorted(self, items, *args, **kwargs):46        kwargs["key"] = self.key_func47        return sorted(items, *args, **kwargs)48    @property49    def key_func(self):50        return cmp_to_key(self.cmp_items)51    def cmp_items(self, a, b):52        if b in self._greater_than.get(a, {}):53            return 154        if a in self._greater_than.get(b, {}):55            return -156        return 057class Undo(Exception):58    """ Raised when we want to stop """59def pick_one(a, b):60    print("Which is greater?")61    print(f"1. {a}")62    print(f"2. {b}")63    while True:64        result = input("> ")65        if result == "1":66            return a, b67        if result == "2":68            return b, a69        if result == "u":70            raise Undo()71def main():72    items = set(sys.argv[1:])73    if not items:74        print("Warning: No items specified", file=sys.stderr)75        return76    r = Ranker.new(items)77    rankings = []78    s = r.sample()79    while s:80        print()81        print("List:", ", ".join(r.sorted_items(reverse=True)))82        print()83        try:84            larger, smaller = pick_one(*list(s))85        except KeyboardInterrupt:86            break87        except Undo:88            try:89                r = rankings.pop()90            except IndexError:91                pass92            continue93        rankings.append(r)94        r = r.add_ranking(larger, smaller)95        s = r.sample()96    if not r.is_complete():97        print("Warning: Ranking is incomplete")98    for x in r.sorted_items(reverse=True):99        print(x)100if __name__ == "__main__":...

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