for position in algo.account.positions:
if position.type_ == 'Long':
algo.account.close_position(position, 1, exit_price)
if today['close'] > yesterday['close']:
entry_capital = algo.account.buying_power
if entry_capital > 0.0001:
algo.account.enter_position('Long', entry_capital, today['close'])
# Data settings
pair = ['ETH', 'BTC'] # Use ETH pricing data on the BTC market
days_history = 360 # From there collect X days of data
exchange = 'Bitfinex'
# Request data from cryptocompare.com
df = cc.load_dataframe(pair, days_history, exchange)
# Algorithm settings
sim_params = {
'capital_base': 1000,
'fee': {
'Long': 0.0025 + 0.001, # fee + spread
'Short': 0.0025 + 0.001,
}
}
r = Gemini(logic=logic, sim_params=sim_params, analyze=analyze_mpl)
# start backtesting custom logic with 1000 (BTC) intital capital
r.run(df, title='History: {}'.format(days_history), show_trades=True)
'date': today['date'],
'price': current_price,
'rsi': rsi[-1],
})
# Data settings
pair = "ETC_BTC" # Use ETH pricing data on the BTC market
period = 300 # Use 1800 second candles
days_history = 30 # From there collect 60 days of data
RSI_OPEN = 55
RSI_DEVIATION = 10
RSI_PERIOD = 14
# Request data from Poloniex
df = px.load_dataframe(pair, period, days_history)
# Algorithm settings
sim_params = {
'capital_base': 1,
'data_frequency': '30T',
'fee': {
'Long': 0.0025,
'Short': 0.0025,
}
}
gemini = Gemini(logic=logic, sim_params=sim_params, analyze=None)
gemini.run(df)
######
######
# get data from file stored on disk
start_date = '2017-01-04 0:00:00'
end_date = '2017-12-31 23:59:59'
base_currency = 'BTC'
other_currency = 'XRP'
frequency = '1D'
df = get_historical_data_from_file(base_currency, other_currency, frequency, start_date, end_date)
########
# Algorithm settings
sim_params = {
'capital_base': 10, # initial capital in BTC
'fee': {
'Long': 0.0015, # fee settings for Long
'Short': 0.0015, # fee settings for Short
},
'data_frequency': '1D' # Time frame to use (see /helpers/timeframe_resampler.py for more info
}
gemini = Gemini(logic=logic, sim_params=sim_params, analyze=analyze_bokeh)
# start backtesting custom logic with 10 (BTC) intital capital
gemini.run(df, show_trades=True)
elif today['close'] > yesterday['close']:
risk = 0.03
entry_price = today['close']
entry_capital = algo.account.buying_power * risk
if entry_capital >= 0.00001:
algo.account.enter_position('Long', entry_capital, entry_price)
# Data settings
pair = "ETH_BTC" # Use ETH pricing data on the BTC market
period = 1800 # Use 1800 second candles
days_history = 60 # Collect 60 days data
# Request data from Poloniex
df = px.load_dataframe(pair, period, days_history)
# Algorithm settings
sim_params = {
'capital_base': 10, # initial capital in BTC
'fee': {
'Long': 0.0015, # fee settings for Long
'Short': 0.0015, # fee settings for Short
},
'data_frequency':
'4H' # Time frame to use (see /helpers/timeframe_resampler.py for more info
}
gemini = Gemini(logic=logic, sim_params=sim_params, analyze=analyze_bokeh)
# start backtesting custom logic with 10 (BTC) intital capital
gemini.run(df, show_trades=False)
'long': long[-1],
})
# Data settings
pair = "ETH_BTC" # Use ETH pricing data on the BTC market
period = 1800 # Use 1800 second candles
days_history = 300 # From there collect 60 days of data
# Request data from Poloniex
df = px.load_dataframe(pair, period, days_history)
# Algorithm settings
sim_params = {
'capital_base': 10,
'fee': {
'Long': 0.0025,
'Short': 0.0025,
},
'data_frequency': '30T'
}
gemini = Gemini(initialize=initialize,
logic=logic,
sim_params=sim_params,
analyze=analyze_bokeh)
# start backtesting custom logic with 10 (BTC) intital capital
gemini.run(df,
title='SMA Cross {}: {}'.format(pair, days_history),
show_trades=True)
algo.records.append({
'date': algo.account.date,
'price': current_price,
'sma20': short[-1],
'sma100': long[-1],
})
pair = ['BTC', 'USD'] # Use ETH pricing data on the BTC market
days_history = 300 # From there collect X days of data
exchange = 'Bitstamp'
# Request data from cryptocompare.com
df = cc.load_dataframe(pair, days_history, exchange)
# Algorithm settings
sim_params = {
'capital_base': 10000,
'fee': {
'Long': 0.0025 + 0.001, # fee + spread
'Short': 0.0025 + 0.001,
}
}
gemini = Gemini(logic=logic, sim_params=sim_params, analyze=analyze_bokeh)
# start backtesting custom logic with 1000 (BTC) intital capital
gemini.run(df,
title='SMA 5x30 History: {}'.format(days_history),
show_trades=True)
'price': current_price,
'short': short[-1],
'long': long[-1],
})
# Data settings
pair = "BCH_BTC" # Use ETH pricing data on the BTC market
period = 1800 # Use 1800 second candles
days_history = 200 # From there collect 60 days of data
# Request data from Poloniex
df = px.load_dataframe(pair, period, days_history)
# Algorithm settings
sim_params = {
'capital_base': 10,
'fee': {
'Long': 0.0015,
'Short': 0.0015,
},
'data_frequency': '2H'
}
gemini = Gemini(initialize=initialize,
logic=logic,
sim_params=sim_params,
analyze=analyze_bokeh)
gemini.run(df,
title='EMA 5x30 {}: {}'.format(pair, days_history),
show_trades=True)