def __init__(self, db=False):
self.loader = _DataLoader(db=db, ib=False)
self.master_screener = pd.DataFrame()
self.strategy = FabStrategy()
self.tf_delay_match = {
235: 0,
145: 7,
210: 5,
165: 4,
130: 13,
220: 12
}
self.list_of_enters = {
('Rule 1', 'Long'): self.strategy.rule_1_buy_enter,
('Rule 1', 'Short'): self.strategy.rule_1_short_enter,
('Rule 2', 'Long'): self.strategy.rule_2_buy_enter,
('Rule 2', 'Short'): self.strategy.rule_2_short_enter,
('Rule 3', 'Long'): self.strategy.rule_3_buy_enter,
('Rule 3', 'Short'): self.strategy.rule_3_short_enter
}
self.list_of_exits = {
('Rule 1', 'Long'): self.strategy.rule_1_buy_exit,
('Rule 1', 'Short'): self.strategy.rule_1_short_exit,
('Rule 2', 'Long'): self.strategy.rule_2_buy_stop_absolute,
('Rule 2', 'Short'): self.strategy.rule_2_short_stop_absolute,
('Rule 3', 'Long'): self.strategy.rule_1_buy_exit,
('Rule 3', 'Short'): self.strategy.rule_1_short_exit
}
def __init__(self):
self.start = False
self.client = None
self.capital = None
self.leverage = 1 / 1000
self.tf = None
self.symbol = None
self.loader = _DataLoader()
self.df = None
def __init__(self):
self.end = datetime.today().strftime('%Y-%m-%d')
self.loader = _DataLoader()
self.range_data = None
self.symbol_data = None
self.start = None
self.summary = "Nothing to Show Yet"
self.tf = None
self.trade_history = TradeHistory()
self.tf_data = None
def optimal_sensitivity_calculator(self, symbol_list, sensitivity_list, tf, delay, rule, side):
df = pd.DataFrame()
for symbol in symbol_list:
self.loader = _DataLoader(db=True)
self.load_backtesting_data(symbol=symbol, all_data=True)
for sensitivity in sensitivity_list:
print(symbol, sensitivity)
self.start_backtesting(tf=tf, delay=delay, sensitivity=sensitivity)
row = self.generate_asset_metrics(self.detailed_th, f'Rule {rule}', side, sensitivity=sensitivity)
df = df.append(row)
return df
def __init__(self):
self.live_trade_history = TradeHistory()
self.trade_journal = pd.DataFrame()
self.dic = {"BUY": "Long", "SELL": "Short"}
self.inverse_dic = dict((y, x) for x, y in self.dic.items())
self.latest_trade_info = None
self.latest_trade = None
self.loader = _DataLoader(db=False)
self.precisions = {
symbol_dic['symbol']: symbol_dic['quantityPrecision']
for symbol_dic in self.loader.binance.futures_exchange_info()
['symbols']
}
def __init__(self, db=True):
self.loader = _DataLoader(db=db)
self.analyzer = Analyzer()
self.date_range = None
self.symbol_data = None
self.start = None
self.end = datetime.today().strftime('%Y-%m-%d')
self.tf = None
self.df = None
self.df_th = None
self.df_tf = None
self.trade_history = TradeHistory()
self.illustrator = Illustrator()
self.strategy = FabStrategy()
self.tf_data = None
self.pnl = None
self.trades = None
self.summary = "Nothing to Show Yet"
def __init__(self, binance=True, qtrade=False, db=False, ib=False):
if binance:
self.binance = Client()
# self.ftx = FtxClient()
self.capital = None
self.leverage = 1
self.tf = None
self.executor = TradeExecutor()
self.loader = _DataLoader(db=db, qtrade=qtrade, ib=ib)
self.illustrator = Illustrator()
self.strategy = FabStrategy()
self.order_history = pd.DataFrame()
self.trade_replay = []
self.precisions = {
symbol_dic['symbol']: symbol_dic['quantityPrecision']
for symbol_dic in self.loader.binance.futures_exchange_info()
['symbols']
}
def optimal_tf_calculator(self, symbols_list, tf_list, delay_list):
df = pd.DataFrame()
for symbol in symbols_list:
self.loader = _DataLoader(db=True)
self.load_backtesting_data(symbol=symbol, all_data=True)
if len(self.df) == 0:
continue
for tf in tf_list:
for delay in delay_list:
print(symbol, tf, delay)
self.start_backtesting(tf, delay=delay)
if len(self.trade_history) in [0,1,2]:
continue
for rule in [1, 2, 3]:
for side in ['Long', 'Short']:
df = pd.concat([df, self.generate_asset_metrics(self.detailed_th, f'Rule {rule}', side, delay=delay)])
print('saved', symbol)
df.to_csv(f"Full TF Delay Backtest {symbols_list[0]} to {symbols_list[-1]}.csv")
return df