from blankly import Alpaca, CoinbasePro

stocks = Alpaca()
crypto = CoinbasePro()
futures = BinanceFutures()

# Easily perform the same actions across exchanges & asset types
stocks.interface.market_order('AAPL', 'buy', 1)
crypto.interface.market_order('BTC-USD', 'buy', 1)
# Full futures featureset
futures.interface.get_hedge_mode()

import blankly
"""
This example shows how backtest over tweets
"""

class TwitterBot(blankly.Model):
    def main(self, args):
        while self.has_data:
            self.backtester.value_account()
            self.sleep('1h')

    def event(self, type_: str, data: str):
        # Now check if it's a tweet about Tesla
        if 'tsla' in data.lower() or 'gme' in data.lower():
            # Buy, sell or evaluate your portfolio
            pass


if __name__ == "__main__":
    exchange = blankly.Alpaca()
    model = TwitterBot(exchange)

    # Add the tweets json here
    model.backtester.add_custom_events(blankly.data.JsonEventReader('./tweets.json'))
    # Now add some underlying prices at 1 month
    model.backtester.add_prices('TSLA', '1h', start_date='3/20/22', stop_date='4/15/22')

    # Backtest or run live
    print(model.backtest(args=None, initial_values={'USD': 10000}))

# Just turn this
strategy.backtest(to='1y')
# Into this
strategy.start()

import blankly
from blankly import StrategyState


def price_event(price, symbol, state: StrategyState):
    """ This function will give an updated price every 15 seconds from our definition below """
    state.variables['history'].append(price)
    rsi = blankly.indicators.rsi(state.variables['history'])
    
    if rsi[-1] < 30 and not state.variables['has_bought']:
        # Dollar cost average buy
        state.variables['has_bought'] = True
        state.interface.market_order(symbol, side='buy', size=1)
    elif rsi[-1] > 70 and state.variables['has_bought']:
        # Dollar cost average sell
        state.variables['has_bought'] = False
        state.interface.market_order(symbol, side='sell', size=1)


def init(symbol, state: StrategyState):
    # Download price data to give context to the algo
    state.variables['history'] = state.interface.history(symbol, to='1y', return_as='list')['open']
    state.variables['has_bought'] = False


if __name__ == "__main__":
    # Authenticate on alpaca to create a strategy
    alpaca = blankly.Alpaca()

    # Use our strategy helper on alpaca
    strategy = blankly.Strategy(alpaca)

    # Run the price event function every time we check for a new price - by default that is 15 seconds
    strategy.add_price_event(price_event, symbol='NCLH', resolution='30m', init=init)
    strategy.add_price_event(price_event, symbol='CRBP', resolution='1h', init=init)
    strategy.add_price_event(price_event, symbol='D', resolution='15m', init=init)
    strategy.add_price_event(price_event, symbol='GME', resolution='30m', init=init)

    # Start the strategy. This will begin each of the price event ticks
    # strategy.start()
    # Or backtest using this
    strategy.backtest(to='1y')