async def main():
init_logger(level=logging.DEBUG)
exchange_factory = class_builders.get("market").get("binance")()
desired_fields = ["open_ts", "open", "close_ts"]
async with exchange_factory.create_data_homogenizer() \
as binance_homogenizer:
base_assets = await binance_homogenizer.get_all_base_assets()
print(f"All the base assets available on the "
f"Binance exchange are {base_assets}")
reference_assets = await binance_homogenizer.get_all_refernce_assets()
print(f"All the reference assets available on the"
f" Binance exchange are {reference_assets}")
time_range = {("25 Jan 2018", "27 Feb 2018"): "1d"}
time_aggregated_data_container = data_container_access.\
TimeAggregatedDataContainer(
exchange_factory,
base_assets=["NANO"],
reference_assets=["BTC", "USDT"],
ohlcv_fields=desired_fields,
time_range_dict=time_range
)
xdataarray_of_coins = await time_aggregated_data_container.get_time_aggregated_data_container()
pprint(xdataarray_of_coins)
xdataset = xdataarray_of_coins.to_dataset("ohlcv_fields")
pprint(xdataset)
async def main():
init_logger(level=logging.DEBUG)
exchange_factory = class_builders.get("market").get("binance")()
async with exchange_factory.create_data_homogenizer() \
as binance_homogenizer:
base_assets = await binance_homogenizer.get_all_base_assets()
desired_fields = ["open_ts", "open", "close"]
candle_type = "1m"
time_aggregated_data_container = data_container_access.TimeAggregatedDataContainer.create_instance(
exchange_factory,
base_assets=base_assets,
reference_assets=["BTC"],
ohlcv_fields=desired_fields,
time_range_dict={("25 Jan 2015", "9 Nov 2020"): candle_type})
xdataarray_of_coins = await time_aggregated_data_container.get_time_aggregated_data_container(
)
type_converter = data_container_post.TypeConvertedData(exchange_factory)
type_converted_dataarray = type_converter.set_type_on_dataarray(
xdataarray_of_coins)
sql_writer = class_builders.get("write_to_disk").get("sqlite")()
with tempfile.NamedTemporaryFile(suffix=".db") as temp_db:
save_to_disk.write_coin_history_to_file(type_converted_dataarray,
sql_writer, temp_db.name,
["open", "close"])
async def main():
init_logger(level=logging.DEBUG)
exchange_factory = class_builders.get("market").get("binance")()
desired_fields = ["open_ts", "open", "close_ts"]
time_aggregated_data_container = data_container_access.TimeAggregatedDataContainer(
exchange_factory,
base_assets=["NANO", "AMB", "XRP"],
reference_assets=["BTC", "USDT"],
ohlcv_fields=desired_fields,
time_range_dict={("25 Jan 2018", "27 Feb 2018"): "1d",
("26 Aug 2020", "now"): "1w"}
)
xdataarray_of_coins = await time_aggregated_data_container.get_time_aggregated_data_container()
pprint(xdataarray_of_coins)
xdataset = xdataarray_of_coins.to_dataset("ohlcv_fields")
pprint(xdataset)
type_converter = data_container_post.TypeConvertedData(exchange_factory)
type_converted_dataset = type_converter.set_type_on_dataset(xdataset)
type_converted_dataarray = type_converter.set_type_on_dataarray(xdataarray_of_coins)
incomplete_data_handle = data_container_post.HandleIncompleteData()
entire_na_removed_dataarray = incomplete_data_handle.\
drop_xarray_coins_with_entire_na(type_converted_dataarray)
entire_na_removed_dataset = incomplete_data_handle.\
drop_xarray_coins_with_entire_na(type_converted_dataset)
strict_na_dropped_dataarray = incomplete_data_handle.\
nullify_incomplete_data_from_dataarray(entire_na_removed_dataarray)
pprint(strict_na_dropped_dataarray)
strict_na_dropped_dataset = incomplete_data_handle.\
nullify_incomplete_data_from_dataset(entire_na_removed_dataset)
pprint(strict_na_dropped_dataset)
def main():
init_logger(logging.DEBUG)
overall_start = datetime(day=25, month=8, year=2018)
overall_end = datetime(day=18, month=11, year=2020)
reference_coin = "BTC"
ohlcv_field = "open"
candle = "1h"
interval = "1d"
data_source_general = "sqlite"
data_source_specific = "binance"
time_interval_iterator = TimeIntervalIterator(overall_start,
overall_end,
interval,
forward_in_time=False,
increasing_range=False)
table_name_list = [
f"COIN_HISTORY_{ohlcv_field}_{reference_coin}_1d",
f"COIN_HISTORY_{ohlcv_field}_{reference_coin}_1h"
]
sqlite_access_creator = class_builders.get("access_xarray").get(
data_source_general)()
full_dataarray = store_largest_xarray(
sqlite_access_creator,
overall_start=overall_start,
overall_end=overall_end,
candle=candle,
reference_coin=reference_coin,
ohlcv_field=ohlcv_field,
file_path=
"/Users/vikram/Documents/Personal/s3_sync/25_Jan_2017_TO_23_May_2021_BTC_1h_1d.db",
mapped_class=OversoldCoins,
table_name_list=table_name_list)
source_iterators = ManualSourceIterators()
success_iterators = ManualSuccessIterators()
iterators = {
"time":
time_interval_iterator,
"source": [source_iterators.high_cutoff, source_iterators.low_cutoff],
"success":
[success_iterators.percentage_increase, success_iterators.days_to_run],
"target": [
"percentage_of_bought_coins_hit_target",
"end_of_run_value_of_bought_coins_if_not_sold",
"end_of_run_value_of_bought_coins_if_sold_on_target"
],
"strategy": [MarketBuyLimitSellIndicatorCreator]
}
pickled_potential_path = str(
pathlib.Path(__file__).parents[4] / "s3_sync" / "staging" /
"1d_2018-07-01_2021-05-20_potential_coins_overall.pickle")
gather_items = gather_overall.GatherIndicator(
full_dataarray,
reference_coin,
ohlcv_field,
iterators,
potential_coin_path=pickled_potential_path,
)
narrowed_start = datetime(day=25, month=8, year=2018)
narrowed_end = datetime(day=17, month=11, year=2020)
collective_ds = gather_items.overall_individual_indicator_calculator(
narrowed_start, narrowed_end)
with open(
pathlib.Path(
pathlib.Path(__file__).parents[2] / "common_db" /
f"success_results_{interval}_"
f"{narrowed_start.strftime('%d-%b-%Y')}_"
f"{narrowed_end.strftime('%d-%b-%Y')}"), "wb") as fp:
pickle.dump(collective_ds, fp)
def main():
init_logger(logging.DEBUG)
overall_start = datetime(day=25, month=8, year=2018)
overall_end = datetime(day=20, month=5, year=2021)
reference_coin = "BTC"
ohlcv_field = "open"
candle = "1h"
interval = "300d"
data_source_general = "sqlite"
data_source_specific = "binance"
time_interval_iterator = TimeIntervalIterator(overall_start,
overall_end,
interval,
forward_in_time=False,
increasing_range=False)
table_name_list = [f"COIN_HISTORY_{ohlcv_field}_{reference_coin}_1d",
f"COIN_HISTORY_{ohlcv_field}_{reference_coin}_1h"]
sqlite_access_creator = class_builders.get("access_xarray").get(data_source_general)()
file_path = str(pathlib.Path(__file__).parents[4] /
"s3_sync" /
"25_Jan_2017_TO_23_May_2021_BTC_1h_1d.db")
full_dataarray_dict = store_largest_xarray(sqlite_access_creator,
overall_start=overall_start,
overall_end=overall_end,
candle=candle,
reference_coin=reference_coin,
ohlcv_field=ohlcv_field,
file_path=file_path,
mapped_class=OversoldCoins,
table_name_list=table_name_list)
source_iterators = ManualSourceIterators()
success_iterators = ManualSuccessIterators()
iterators = {"time": time_interval_iterator,
"source": [
source_iterators.cutoff_mean,
source_iterators.cutoff_deviation,
source_iterators.max_coins_to_buy
],
"success": [
success_iterators.percentage_increase,
success_iterators.percentage_reduction,
success_iterators.days_to_run,
success_iterators.stop_price_sell,
success_iterators.limit_sell_adjust_trail
],
"target": [
"calculate_end_of_run_value"
],
"strategy":
[
MarketBuyTrailingSellSimulationCreator
]
}
pickled_potential_path = str(pathlib.Path(__file__).parents[4] /
"s3_sync" /
"staging" /
"1d_2018-07-01_2021-05-20_potential_coins_overall.pickle")
gather_items = gather_overall.GatherSimulation(
full_dataarray_dict,
reference_coin,
ohlcv_field,
iterators,
potential_coin_path=pickled_potential_path,
)
# pickled_potential_path = str(pathlib.Path(pathlib.Path(__file__).resolve().parents[3] /
# "common_db" /
# f"1h_2018_to_2020_potential_coins.pickled"))
narrowed_start = datetime(day=25, month=8, year=2018)
narrowed_end = datetime(day=20, month=5, year=2021)
collective_ds = gather_items.simulation_calculator(narrowed_start,
narrowed_end,
)
with open("/Users/vikram/Documents/Personal/s3_sync/result_temp_1",
"wb") as fp:
pickle.dump(collective_ds, fp)
def main():
init_logger(logging.DEBUG)
overall_start = datetime(day=25, month=8, year=2018)
overall_end = datetime(day=20, month=5, year=2021)
reference_coin = "BTC"
ohlcv_field = "open"
candle = "1h"
data_source_general = "sqlite"
data_source_specific = "binance"
table_name_list = [
f"COIN_HISTORY_{ohlcv_field}_{reference_coin}_1d",
f"COIN_HISTORY_{ohlcv_field}_{reference_coin}_1h"
]
sqlite_access_creator = class_builders.get("access_xarray").get(
data_source_general)()
full_history_da_dict = store_largest_xarray(
sqlite_access_creator,
overall_start=overall_start,
overall_end=overall_end,
candle=candle,
reference_coin=reference_coin,
ohlcv_field=ohlcv_field,
file_path=
"/Users/vikram/Documents/Personal/s3_sync/25_Jan_2017_TO_23_May_2021_BTC_1h_1d.db",
mapped_class=OversoldCoins,
table_name_list=table_name_list)
source_iterators = ManualSourceIterators()
interval = "1d"
time_interval_iterator = TimeIntervalIterator(overall_start,
overall_end,
interval,
forward_in_time=False,
increasing_range=False)
iterators = {
"time": time_interval_iterator,
"source": [source_iterators.high_cutoff, source_iterators.low_cutoff],
"success": [],
"target": [],
"strategy": []
}
gather_items = gather_overall.GatherPotential(
full_history_da_dict,
reference_coin,
ohlcv_field,
iterators,
potential_coin_path=None,
)
narrowed_start = datetime(day=1, month=7, year=2018)
narrowed_end = datetime(day=20, month=5, year=2021)
gather_items.store_potential_coins_pickled(pickled_file_path=str(
pathlib.Path(
pathlib.Path(__file__).parents[4] / "s3_sync" / "staging" /
f"{interval}_"
f"{narrowed_start.year}-{narrowed_start.month}-{narrowed_start.day}_"
f"{narrowed_end.year}-{narrowed_end.month}-{narrowed_end.day}_"
f"potential_coins_overall.pickle")),
narrowed_start_time=
narrowed_start,
narrowed_end_time=narrowed_end)