How to use technical - 10 common examples

def compute_interval(dataframe: DataFrame, exchange_interval=False):
    """
        calculates the interval of the given dataframe for us
    :param dataframe:
    :param exchange_interval: should we convert the result to an exchange interval or just a number
    :return:
    """
    res_interval = int((dataframe['date'] - dataframe['date'].shift()).min().total_seconds() // 60)

    if exchange_interval:
        # convert to our allowed ticker values
        from technical.exchange import TICKER_INTERVAL_MINUTES
        converted = list(TICKER_INTERVAL_MINUTES.keys())[
            list(TICKER_INTERVAL_MINUTES.values()).index(exchange_interval)]
        if len(converted) > 0:
            return converted
        else:
            raise Exception(
                f"sorry, your interval of {res_interval} is not "
                f"supported in {TICKER_INTERVAL_MINUTES}")

    return res_interval

def compute_interval(dataframe: DataFrame, exchange_interval=False):
    """
        calculates the interval of the given dataframe for us
    :param dataframe:
    :param exchange_interval: should we convert the result to an exchange interval or just a number
    :return:
    """
    res_interval = int((dataframe['date'] - dataframe['date'].shift()).min().total_seconds() // 60)

    if exchange_interval:
        # convert to our allowed ticker values
        from technical.exchange import TICKER_INTERVAL_MINUTES
        converted = list(TICKER_INTERVAL_MINUTES.keys())[
            list(TICKER_INTERVAL_MINUTES.values()).index(exchange_interval)]
        if len(converted) > 0:
            return converted
        else:
            raise Exception(
                f"sorry, your interval of {res_interval} is not "
                f"supported in {TICKER_INTERVAL_MINUTES}")

    return res_interval

:param dataframe:
    :param exchange_interval: should we convert the result to an exchange interval or just a number
    :return:
    """
    res_interval = int((dataframe['date'] - dataframe['date'].shift()).min().total_seconds() // 60)

    if exchange_interval:
        # convert to our allowed ticker values
        from technical.exchange import TICKER_INTERVAL_MINUTES
        converted = list(TICKER_INTERVAL_MINUTES.keys())[
            list(TICKER_INTERVAL_MINUTES.values()).index(exchange_interval)]
        if len(converted) > 0:
            return converted
        else:
            raise Exception(
                f"sorry, your interval of {res_interval} is not "
                f"supported in {TICKER_INTERVAL_MINUTES}")

    return res_interval

def test_resampled_merge(testdata_1m_btc):
    resampled = resample_to_interval(testdata_1m_btc, 5)

    merged = resampled_merge(testdata_1m_btc, resampled)

    assert (len(merged) == len(testdata_1m_btc))
    assert "resample_5_open" in merged
    assert "resample_5_close" in merged
    assert "resample_5_low" in merged
    assert "resample_5_high" in merged

    assert "resample_5_date" not in merged
    assert "resample_5_volume" not in merged
    # Verify the assignment goes to the correct candle
    # If resampling to 5m, then the resampled value needs to be on the 5m candle.
    assert sum(merged.loc[merged['date'] == '2017-11-14 22:54:00', 'resample_5_close'].isna()) == 1
    assert sum(merged.loc[merged['date'] == '2017-11-14 22:55:00', 'resample_5_close'].isna()) == 0
    assert sum(merged.loc[merged['date'] == '2017-11-14 22:56:00', 'resample_5_close'].isna()) == 1

def test_resampled_merge_contains_indicator(testdata_1m_btc):
    resampled = resample_to_interval(testdata_1m_btc, 5)
    resampled['cmf'] = chaikin_money_flow(resampled, 5)
    merged = resampled_merge(testdata_1m_btc, resampled)

    print(merged)
    assert "resample_5_cmf" in merged

def test_resample_to_interval(testdata_1m_btc):
    result = resample_to_interval(testdata_1m_btc, 5)

    # should be roughly a factor 5
    assert (len(testdata_1m_btc) / len(result) > 4.5)
    assert (len(testdata_1m_btc) / len(result) < 5.5)

def test_historical_data():
    days = datetime.datetime.today() - datetime.timedelta(days=7)
    print(days)
    data = historical_data(
        "USDT", "BNB", "1d", days.timestamp())

    assert len(data) == 7

def test_historical_data_ploniex_long():
    """ this one is awesome since you can download years worth of data"""
    days = datetime.datetime.today() - datetime.timedelta(days=365)

    data = historical_data(
        "BTC", "ETC", "1d", days.timestamp(), "poloniex")

    assert len(data) == 365

def test_historical_data_ploniex():
    """ this one is awesome since you can download years worth of data"""
    days = datetime.datetime.today() - datetime.timedelta(days=90)

    data = historical_data(
        "BTC", "ETH", "1d", days.timestamp(), "poloniex")

    assert len(data) == 90

def test_resampled_merge(testdata_1m_btc):
    resampled = resample_to_interval(testdata_1m_btc, 5)

    merged = resampled_merge(testdata_1m_btc, resampled)

    assert (len(merged) == len(testdata_1m_btc))
    assert "resample_5_open" in merged
    assert "resample_5_close" in merged
    assert "resample_5_low" in merged
    assert "resample_5_high" in merged

    assert "resample_5_date" not in merged
    assert "resample_5_volume" not in merged
    # Verify the assignment goes to the correct candle
    # If resampling to 5m, then the resampled value needs to be on the 5m candle.
    assert sum(merged.loc[merged['date'] == '2017-11-14 22:54:00', 'resample_5_close'].isna()) == 1
    assert sum(merged.loc[merged['date'] == '2017-11-14 22:55:00', 'resample_5_close'].isna()) == 0
    assert sum(merged.loc[merged['date'] == '2017-11-14 22:56:00', 'resample_5_close'].isna()) == 1