CoCalc -- History

GitHub Repository: wiseplat/python-code
Path: blob/master/ invest-robot-contest_TI_API_2-master/History_check.py
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# Этот файл заточен под работу с фьючерсами, для акций нужно изменить параметры.
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# Плюс сейчас не хавтает аналитической части,
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# она происходит уже в самой табдице (Посредством Google Sheets например).
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import datetime
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import math
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import pandas as pd
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# import matplotlib.pyplot as plt
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import tinkoff.invest
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import intro.basek
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import intro.accid
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from intro.quotation_dt import quotation_count
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dtypes_dic = {'time': str,
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              'volume': int,
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              'open': float,
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              'close': float,
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              'high': float,
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              'low': float,
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              'ma': float,
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              'ema': float
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              }
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# Часть для обработки файла с данными
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hist_candles_inst = pd.read_csv('csv_files/brent092022_7-8mnth.csv', dtype=dtypes_dic)
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# Здесь запросы токена и id из stub файлов, и самого клиента из библиотеки Тинькофф
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TOKEN = intro.basek.TINKOFF_INVEST_DOG_NEW
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SDK_client = tinkoff.invest.Client(TOKEN)
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User_acc_ID = intro.accid.ACC_ID
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instrument_figi = 'FUTBR0922000'
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open_positions_limit = 60000
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close_time = datetime.time.fromisoformat('15:30:00')
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count_time_base = datetime.datetime(1,1,1, close_time.hour, close_time.minute, close_time.second)
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close_period_start = datetime.timedelta(minutes=1)
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close_period_start = count_time_base + close_period_start
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close_period_end = datetime.timedelta(minutes=30)
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close_period_end = count_time_base + close_period_end
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def future_structure(future: [tinkoff.invest.GetFuturesMarginResponse]):
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    future_i = pd.DataFrame([{
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        'initial_margin_on_buy': quotation_count(future.initial_margin_on_buy),
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        'initial_margin_on_sell': quotation_count(future.initial_margin_on_sell),
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        'min_price_increment': quotation_count(future.min_price_increment),
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        'min_price_increment_amount': quotation_count(future.min_price_increment_amount)
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    }])
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    return future_i
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def short_long(instrument: [tinkoff.invest.Future]):
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    future_i_2 = pd.DataFrame([{
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        'klong': quotation_count(instrument.klong),
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        'kshort': quotation_count(instrument.kshort),
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        'last_trade_day': pd.Timestamp(instrument.last_trade_date)
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    }])
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    return future_i_2
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def future_info():
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    with SDK_client as client:
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        future_info_not_structured = client.instruments.get_futures_margin(figi=instrument_figi)
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        short_long_coef = client.instruments.future_by(id_type=1, id=instrument_figi).instrument
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        future_info_structured = future_structure(future_info_not_structured)
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        appendix_1 = short_long(short_long_coef)
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        fi = pd.concat([future_info_structured, appendix_1], axis=1)
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        return fi
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future_info_1 = future_info()
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fi_a = future_info_1['min_price_increment'][0]
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fi_b = future_info_1['min_price_increment_amount'][0]
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fi_shortk = future_info_1['klong'][0]
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fi_lolgk = future_info_1['kshort'][0]
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fi_time = future_info_1['last_trade_day'][0]
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def ma_ema_cross_strategy_test(historical_candles_df):
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    hcdf = historical_candles_df
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    open_positions = 0  # Счётчик открытых позиций
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    check_rule = [bool, bool, bool]  # Массив для проверки по правилам
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    condition_sell = [False, True, False]  # Правило 1
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    condition_buy = [True, False, True]  # Правило 2
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    ma = hcdf['ma']
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    ema = hcdf['ema']
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    time_line = hcdf['time']
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    hcdf['contract_turnover'] = 0
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    hcdf['deal'] = pd.NaT
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    hcdf['commission'] = 0
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    order_positions = 0
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    hcdf.drop(columns=['Unnamed: 0'], inplace=True)
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    # Проверка достаточности лимита/денег на счёте для открытия позиции
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    if open_positions_limit < (hcdf.loc[0, 'close'] * fi_b / fi_a):
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        print("Not enough money to open position or low limit")
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        exit()
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    else:
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        print('Limit Success')
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    for indx in range(len(hcdf)):
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        indx_min_1 = indx - 1
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        if indx_min_1 < 0:
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            continue
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        # Условия. В данном случае это проверка изменения соотношения MA и EMA средних с помощью 3-х соотношений.
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        # 1 Проверка отношения значения EMA в текущем периоде к предыдущему
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        if ema[indx] > ema[indx_min_1]:
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            check_rule[0] = True
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        else:
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            check_rule[0] = False
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        # 2 Проверка отношения значения MA в текущем периоде к EMA в текущем периоде
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        if ma[indx] > ema[indx]:
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            check_rule[1] = True
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        else:
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            check_rule[1] = False
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        # 3 Проверка отношения значения MA в предыдущем периоде к EMA в предыдущем
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        if ma[indx_min_1] > ema[indx_min_1]:
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            check_rule[2] = True
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        else:
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            check_rule[2] = False
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        # Расчёт открываемой позиции
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        if open_positions == 0 and check_rule == condition_sell:
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            order_positions = -1 * math.floor(open_positions_limit/(hcdf.loc[indx, 'close'] * fi_b/fi_a))
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            # print('Order positions:', order_positions, 'condition_sell from 0')
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        elif open_positions == 0 and check_rule == condition_buy:
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            order_positions = math.floor(open_positions_limit/(hcdf.loc[indx, 'close'] * fi_b/fi_a))
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            # print('Order positions:', order_positions, 'condition_buy from 0')
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        elif indx == len(hcdf) - 2:
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            order_positions = -1 * open_positions
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            # print('Order positions:', order_positions, 'condition_close when close to the end of massive')
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        elif open_positions < 0:
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            order_positions = 2 * math.floor(open_positions_limit/(hcdf.loc[indx, 'close'] * fi_b/fi_a))
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            # print('Order positions:', order_positions, 'condition_buy from open short positions')
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        elif open_positions > 0:
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            order_positions = -2 * math.floor(open_positions_limit/(hcdf.loc[indx, 'close'] * fi_b/fi_a))
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            # print('Order positions:', order_positions, 'condition_buy from open short positions')
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        # print(pd.Timestamp(time_line[indx]).day_of_week, time_line[indx], pd.Timestamp(time_line[indx]).ctime())
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        hcdf.loc[indx, 'open_position'] = open_positions
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        if close_period_end.time() >= pd.Timestamp(time_line[indx]).time() >= close_period_start.time():
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            continue
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        elif open_positions != 0 and \
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            pd.Timestamp(time_line[indx]).day_of_week == 4 and \
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            pd.Timestamp(time_line[indx]).time() >= close_time:
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            hcdf.loc[indx, 'deal'] = 'week end'
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            order_positions = open_positions
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            hcdf.loc[indx, 'contract_turnover'] = -1 * order_positions * (hcdf.loc[indx, 'close'] / fi_a * fi_b)
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            hcdf.loc[indx, 'commission'] = abs(order_positions * (hcdf.loc[indx, 'close'] / fi_a * fi_b) * 0.0004)
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            hcdf.loc[indx, 'open_position'] = open_positions + -1 * order_positions
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            hcdf.loc[indx, 'order_position'] = -1 * order_positions
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            open_positions = open_positions + -1 * order_positions
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            continue
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        if check_rule == condition_buy and open_positions <= 0:
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            hcdf.loc[indx, 'deal'] = 'buy'
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            hcdf.loc[indx, 'contract_turnover'] = order_positions * (hcdf.loc[indx, 'close'] / fi_a * fi_b)
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            hcdf.loc[indx, 'commission'] = abs(order_positions * (hcdf.loc[indx, 'close'] / fi_a * fi_b) * 0.0004)
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            hcdf.loc[indx, 'open_position'] = open_positions + order_positions
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            hcdf.loc[indx, 'order_position'] = order_positions
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            open_positions = open_positions + order_positions
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        elif check_rule == condition_sell and open_positions >= 0:
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            hcdf.loc[indx, 'deal'] = 'sell'
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            hcdf.loc[indx, 'contract_turnover'] = order_positions * (hcdf.loc[indx, 'close'] / fi_a * fi_b)
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            hcdf.loc[indx, 'commission'] = abs(order_positions * (hcdf.loc[indx, 'close'] / fi_a * fi_b) * 0.0004)
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            hcdf.loc[indx, 'open_position'] = open_positions + order_positions
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            hcdf.loc[indx, 'order_position'] = order_positions
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            open_positions = open_positions + order_positions
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        elif indx == len(hcdf) - 10:
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            print('last one done')
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            hcdf.loc[indx, 'deal'] = 'close'
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            hcdf.loc[indx, 'contract_turnover'] = -1 * order_positions / 2 * (hcdf.loc[indx, 'close'] / fi_a * fi_b)
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            hcdf.loc[indx, 'commission'] = abs(-1 * order_positions / 2 * (hcdf.loc[indx, 'close'] / fi_a * fi_b) * 0.0004)
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            hcdf.loc[indx, 'open_position'] = open_positions + order_positions / 2
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            hcdf.loc[indx, 'order_position'] = -1 * order_positions / 2
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            open_positions = open_positions + -1 * order_positions / 2
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            print(open_positions)
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            break
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        else:
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            hcdf.loc[indx, 'open_position'] = open_positions
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            hcdf.loc[indx, 'order_position'] = order_positions
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            open_positions = open_positions
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            # print('no actions')
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    # Далее три опции:
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    # Построение графика при необходимости (нужно еще раскомментировать импорт matplotlib)
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    # with plt.style.context('Solarize_Light2'):
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    #     a = hcdf['time']
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    #     b = hcdf['close']
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    #     c = hcdf['ma']
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    #     d = hcdf['ema']
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    #     plt.plot(a, b, a, c, a, d)
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    #     plt.show()
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    # Вывод полученных значений
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    # hcdf.drop(columns=['Unnamed: 0'], inplace=True)
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    hcdf.dropna(how='any', inplace=True)
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    print(hcdf)
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    # Запись значений в файл со своим названием
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    # hcdf.to_csv('csv_files/brent062022_orders.csv', mode='w')
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    # print('Record to file complete')
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    return hcdf
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def check_profits(operations_df):
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    operations_df.dropna(how='any', inplace=True)
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    trade_result = 0
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    commision_sum = 0
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    # o = operations_df['contract_turnover']
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    # c = operations_df['commission']
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    for i in range(len(operations_df)):
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        trade_result += operations_df['contract_turnover'].iloc[i]
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        commision_sum += operations_df['commission'].iloc[i]
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    return print('trade result = ', trade_result - commision_sum, '\n',
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                 'only operations =', trade_result)
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def record_orders_results(operations_df):
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    operations_df.dropna(how='any', inplace=True)
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    operations_df.to_csv('csv_files/brent092022_7-8mnth_report.csv', mode='w')
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    return print('Record orders results complete')
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test = ma_ema_cross_strategy_test(hist_candles_inst)
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record_orders_results(test)
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report = pd.read_csv('csv_files/brent092022_7-8mnth_report.csv')
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check_profits(report)
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