def rebalance(context, data):
context.i += 1
# skip first LONG_WINDOW bars to fill windows
if context.i < LONG_WINDOW:
return
# get pipeline data for asset of interest
pipeline_data = context.pipeline_data
pipeline_data = pipeline_data[pipeline_data.index == context.asset].iloc[0]
# retrieve long and short moving averages from pipeline
short_mavg = pipeline_data.short_mavg
long_mavg = pipeline_data.long_mavg
price = pipeline_data.price
# check that order has not already been placed
open_orders = get_open_orders()
if context.asset not in open_orders:
# check that the asset of interest can currently be traded
if data.can_trade(context.asset):
# adjust portfolio based on comparison of long and short vwap
if short_mavg > long_mavg:
order_target_percent(context.asset, TARGET_INVESTMENT_RATIO)
elif short_mavg < long_mavg:
order_target_percent(context.asset, 0.0)
record(
price=price,
cash=context.portfolio.cash,
leverage=context.account.leverage,
short_mavg=short_mavg,
long_mavg=long_mavg,
)
def handle_data(context, data):
cash = context.portfolio.cash
target_hodl_value = TARGET_HODL_RATIO * context.portfolio.starting_cash
reserve_value = RESERVE_RATIO * context.portfolio.starting_cash
# Cancel any outstanding orders
orders = get_open_orders(context.asset) or []
for order in orders:
cancel_order(order)
# Stop buying after passing the reserve threshold
if cash <= reserve_value:
context.is_buying = False
# Retrieve current asset price from pricing data
price = data[context.asset].price
# Check if still buying and could (approximately) afford another purchase
if context.is_buying and cash > price:
# Place order to make position in asset equal to target_hodl_value
order_target_value(
context.asset,
target_hodl_value,
limit_price=price * 1.1,
stop_price=price * 0.9,
)
record(
price=price,
cash=cash,
starting_cash=context.portfolio.starting_cash,
leverage=context.account.leverage,
)
def handle_data(context, data):
# define the windows for the moving averages
short_window = 50
long_window = 200
# Skip as many bars as long_window to properly compute the average
context.i += 1
if context.i < long_window:
return
# Compute moving averages calling data.history() for each
# moving average with the appropriate parameters. We choose to use
# minute bars for this simulation -> freq="1m"
# Returns a pandas dataframe.
short_mavg = data.history(context.asset,
'price',
bar_count=short_window,
frequency="1m").mean()
long_mavg = data.history(context.asset,
'price',
bar_count=long_window,
frequency="1m").mean()
# Let's keep the price of our asset in a more handy variable
price = data.current(context.asset, 'price')
# If base_price is not set, we use the current value. This is the
# price at the first bar which we reference to calculate price_change.
if context.base_price is None:
context.base_price = price
price_change = (price - context.base_price) / context.base_price
# Save values for later inspection
record(price=price,
cash=context.portfolio.cash,
price_change=price_change,
short_mavg=short_mavg,
long_mavg=long_mavg)
# Since we are using limit orders, some orders may not execute immediately
# we wait until all orders are executed before considering more trades.
orders = get_open_orders(context.asset)
if len(orders) > 0:
return
# Exit if we cannot trade
if not data.can_trade(context.asset):
return
# We check what's our position on our portfolio and trade accordingly
pos_amount = context.portfolio.positions[context.asset].amount
# Trading logic
if short_mavg > long_mavg and pos_amount == 0:
# we buy 100% of our portfolio for this asset
order_target_percent(context.asset, 1)
elif short_mavg < long_mavg and pos_amount > 0:
# we sell all our positions for this asset
order_target_percent(context.asset, 0)
def _handle_buy_sell_decision(context, data, signal, price):
orders = get_open_orders(context.asset)
if len(orders) > 0:
log.info('skipping bar until all open orders execute')
return
positions = context.portfolio.positions
if context.position is None and context.asset in positions:
position = positions[context.asset]
context.position = dict(
cost_basis=position['cost_basis'],
amount=position['amount'],
stop=None
)
# action = None
if context.position is not None:
cost_basis = context.position['cost_basis']
amount = context.position['amount']
log.info(
'found {amount} positions with cost basis {cost_basis}'.format(
amount=amount,
cost_basis=cost_basis
)
)
stop = context.position['stop']
target = cost_basis * (1 + context.TARGET)
if price >= target:
context.position['cost_basis'] = price
context.position['stop'] = context.STOP
stop_target = context.STOP_LOSS if stop is None else context.STOP
if price < cost_basis * (1 - stop_target):
log.info('executing stop loss')
order(
asset=context.asset,
amount=-amount,
limit_price=price * (1 - context.SLIPPAGE_ALLOWED),
)
# action = 0
context.position = None
else:
if signal == 'long':
log.info('opening position')
buy_amount = context.MAX_HOLDINGS / price
order(
asset=context.asset,
amount=buy_amount,
limit_price=price * (1 + context.SLIPPAGE_ALLOWED),
)
context.position = dict(
cost_basis=price,
amount=buy_amount,
stop=None
)
def rebalance(context, data):
context.i += 1
# Skip first LONG_WINDOW bars to fill windows
if context.i < context.WIN DOW:
return
# Get pipeline data for asset of interest
pipeline_data = context.pipeline_data
pipeline_data = pipeline_data[pipeline_data.index == context.asset].iloc[0]
# Compute the necessary statistics
sma = pipeline_data.sma
std = pipeline_data.std()
price = pipeline_data.price
# Compute buy and sell thresholds
# Buy threshold is the simple moving average value plus one standard dev.
# Sell threshold is the simple moving average value minus one standard dev.
buy_threshold = sma-0.3*std/math.sqrt(context.WINDOW)
sell_threshold = sma+0.3*std/math.sqrt(context.WINDOW)
# Check that the order has not already been placed
open_orders = get_open_orders()
if context.asset not in open_orders:
# check that the asset of interest can currently be traded
if data.can_trade(context.asset):
# Trading logic: if price is less than the buy threshold, mean
# reversion should drive price up. Algorithm invests 100% in the
# asset. In the opposite case, mean reversion should drive price
# down. Algorithm invests 50% in cash and 50% in the asset. If
# price is between buy and sell thresholds, algorithm invests 25%
# in cash and 75% in the asset.
if price < buy_threshold:
order_target_percent(
context.asset,
1.0,
)
elif price > sell_threshold:
order_target_percent(
context.asset,
0.5,
)
else:
order_target_percent(
context.asset,
0.75,
)
record(
price=price,
leverage=context.account.leverage,
sma=sma,
std=std,
buy_threshold=buy_threshold,
sell_threshold=sell_threshold,
)
def _initialize(self):
if self.initialized:
return
if self.is_live:
exchange_orders = get_open_orders(self.asset)
if exchange_orders is None:
self.ignored_orderid = []
else:
for o in exchange_orders:
self.ignored_orderid.append(o.id)
self.initialized = True
def _handle_buy_sell_decision(context, data, signal, price):
orders = get_open_orders(context.asset)
if len(orders) > 0:
log.info('skipping bar until all open orders execute')
return
positions = context.portfolio.positions
if context.position is None and context.asset in positions:
position = positions[context.asset]
context.position = dict(cost_basis=position['cost_basis'],
amount=position['amount'],
stop=None)
action = None
if context.position is not None:
cost_basis = context.position['cost_basis']
amount = context.position['amount']
log.info(
'found {amount} positions with cost basis {cost_basis}'.format(
amount=amount, cost_basis=cost_basis))
stop = context.position['stop']
target = cost_basis * (1 + context.TARGET)
if price >= target:
context.position['cost_basis'] = price
context.position['stop'] = context.STOP
stop_target = context.STOP_LOSS if stop is None else context.STOP
if price < cost_basis * (1 - stop_target):
log.info('executing stop loss')
order(
asset=context.asset,
amount=-amount,
limit_price=price * (1 - context.SLIPPAGE_ALLOWED),
)
action = 0
context.position = None
else:
if signal == 'long':
log.info('opening position')
buy_amount = context.MAX_HOLDINGS / price
order(
asset=context.asset,
amount=buy_amount,
limit_price=price * (1 + context.SLIPPAGE_ALLOWED),
)
context.position = dict(cost_basis=price,
amount=buy_amount,
stop=None)
action = 0
def initialize(self):
if self.initialized:
return
try:
self.is_live = self.context.mode_name == 'live'
except:
self.is_live = False
if self.is_live:
exchange_orders = get_open_orders(self.context.asset)
if exchange_orders is None:
self.ignored_orderid = []
else:
for o in exchange_orders:
self.ignored_orderid.append(o.id)
self.initialized = True
def handle_data(context, data):
sym = symbol("XRP_BTC")
#res = data.can_trade(sym)
#history = data.history(symbol("bch_btc"), ['close'],
# bar_count=11,
# frequency='5T')
#print('\nnow: %s\n%s' % (data.current_dt, history))
#if not hasattr(context, 'i'):
# context.i = 0
#context.i += 1
#print(history)
#get_order(5, return_price=True)
print(get_open_orders(sym))
def rebalance(context, data):
# To make market decisions, we're calculating the token's
# moving average for the last 5 days.
# We get the price history for the last 5 days.
price_history = data.history(context.asset,
fields='price',
bar_count=5,
frequency='1d')
# Then we take an average of those 5 days.
average_price = price_history.mean()
# We also get the coin's current price.
price = data.current(context.asset, 'price')
# Cancel any outstanding orders
orders = get_open_orders(context.asset) or []
for order in orders:
cancel_order(order)
# If our coin is currently listed on a major exchange
if data.can_trade(context.asset):
# If the current price is 1% above the 5-day average price,
# we open a long position. If the current price is below the
# average price, then we want to close our position to 0 shares.
if price > (1.01 * average_price):
# Place the buy order (positive means buy, negative means sell)
order_target_percent(context.asset, .99)
log.info("Buying %s" % (context.asset.symbol))
elif price < average_price:
# Sell all of our shares by setting the target position to zero
order_target_percent(context.asset, 0)
log.info("Selling %s" % (context.asset.symbol))
# Use the record() method to track up to five custom signals.
# Record Apple's current price and the average price over the last
# five days.
cash = context.portfolio.cash
leverage = context.account.leverage
record(price=price,
average_price=average_price,
cash=cash,
leverage=leverage)
def handle_data(context, data):
context.asset = symbol("btc_usd")
current = data.history(context.asset, ['price', 'volume'],
bar_count=1,
frequency='1T')
price = data.current(context.asset, 'price')
volume = data.current(context.asset, 'volume')
#pd.set_option("display.precision", 8)
#print(history)
context.pricing_data = context.pricing_data.append(current)
context.csvwriter.writerow([
current.index[0], current.iloc[0]['price'], current.iloc[0]['volume']
])
if context.index == 0 or context.index == 2 or context.index == 4:
order(context.asset, 1)
for item, orders in get_open_orders().items():
o = orders[0]
get_order(o.sid)
elif context.index == 6:
order(context.asset, -1)
elif context.index == 8:
order(context.asset, -1)
if context.portfolio.positions:
position = context.portfolio.positions[context.asset]
print("index={}".format(context.index))
print("amount={}, cost-basis={}".format(position.amount,
position.cost_basis))
#
# context.csvwriter.writerow([data.current_dt,
# current.iloc[0]['price'],
# current.iloc[0]['volume']])
#print(context.blotter.current_dt)
#print(data.current_dt)
context.index += 1
def handle_data(context, data):
if context.index == 0:
order(symbol("bat_btc"), -9.9, limit_price=0.00002630)
print("blotter={}".format(context.blotter.orders))
#res = data.can_trade(sym)
#history = data.history(symbol("bch_btc"), ['close'],
# bar_count=11,
# frequency='5T')
#print('\nnow: %s\n%s' % (data.current_dt, history))
#if not hasattr(context, 'i'):
# context.i = 0
#context.i += 1
#print(history)
#get_order(5, return_price=True)
print("open orders={}".format(get_open_orders(context.asset)))
context.index += 1
def trade_logic(context, data):
# Cancel any outstanding orders
orders = get_open_orders(context.asset) or []
for order in orders:
cancel_order(order)
# Define base price and make initial trades to achieve target investment ratio of 0.5
order_target_percent(context.asset, 0.5)
# Retrieve current asset price from pricing data
price = data.current(context.asset, "price")
# Compute portfolio cumulative return
Portfolio_cumulative_return = (context.portfolio.portfolio_value /
context.portfolio.starting_cash - 1) * 100
# Save values for later inspection
record(
price=price,
cash=context.portfolio.cash,
leverage=context.account.leverage,
Portfolio_cumulative_return=Portfolio_cumulative_return,
)
def handle_data(context, data):
context.i += 1
starting_cash = context.portfolio.starting_cash
target_hodl_value = context.TARGET_HODL_RATIO * starting_cash
reserve_value = context.RESERVE_RATIO * starting_cash
# Cancel any outstanding orders
orders = get_open_orders(context.asset) or []
for order in orders:
cancel_order(order)
# Stop buying after passing the reserve threshold
cash = context.portfolio.cash
if cash <= reserve_value:
context.is_buying = False
# Retrieve current asset price from pricing data
price = data.current(context.asset, 'price')
# Check if still buying and could (approximately) afford another purchase
if context.is_buying and cash > price:
print('buying')
# Place order to make position in asset equal to target_hodl_value
order_target_value(
context.asset,
target_hodl_value,
limit_price=price * 1.1,
)
record(
price=price,
volume=data.current(context.asset, 'volume'),
cash=cash,
starting_cash=context.portfolio.starting_cash,
leverage=context.account.leverage,
)
def handle_data(context, data):
today = data.current_dt.floor('1D')
if today != context.current_day:
context.current_day = today
context.cmx_invent.cancel_all()
if context.global_mode == 'live':
context.cmx_account.update(data)
context.cmx_risk.adjust_position_limits(data)
context.cmx_risk.run(data)
bid_price = 999
ask_price = 0
quantity = 100
print('{} @ {}'.format(
context.portfolio.positions[context.asset].cost_basis,
context.portfolio.positions[context.asset].amount))
if context.can_buy and context.cmx_risk.position < 1000:
context.cmx_exec.send_orders({bid_price: quantity}, side.buy)
#context.cmx_exec.send_orders({ask_price: -1 * quantity}, side.sell)
else:
context.can_sell = True
context.can_buy = False
if context.can_sell and context.cmx_risk.position > 0:
context.cmx_exec.send_orders({ask_price: -1 * quantity}, side.sell)
if context.cmx_risk.position != 0:
logging.info('tester: risk.position = {} @ {}'.format(
context.cmx_risk.position, context.cmx_risk.ts))
else:
corders = context.blotter.open_orders[context.asset]
eorders = get_open_orders(context.asset)
logging.info('context orders: {}'.format(corders))
logging.info('exchange orders: {}'.format(eorders))
def _handle_data(context, data):
price = data.current(context.asset, 'close')
log.info('got price {price}'.format(price=price))
if price is None:
log.warn('no pricing data')
return
prices = data.history(context.asset,
fields='price',
bar_count=1,
frequency='1m')
rsi = talib.RSI(prices.values, timeperiod=14)[-1]
log.info('got rsi: {}'.format(rsi))
# Buying more when RSI is low, this should lower our cost basis
if rsi <= 30:
buy_increment = 1
elif rsi <= 40:
buy_increment = 0.5
elif rsi <= 70:
buy_increment = 0.1
else:
buy_increment = None
cash = context.portfolio.cash
log.info('base currency available: {cash}'.format(cash=cash))
record(price=price)
orders = get_open_orders(context.asset)
if len(orders) > 0:
log.info('skipping bar until all open orders execute')
return
is_buy = False
cost_basis = None
if context.asset in context.portfolio.positions:
position = context.portfolio.positions[context.asset]
cost_basis = position.cost_basis
log.info(
'found {amount} positions with cost basis {cost_basis}'.format(
amount=position.amount, cost_basis=cost_basis))
if position.amount >= context.TARGET_POSITIONS:
log.info('reached positions target: {}'.format(position.amount))
return
if price < cost_basis:
is_buy = True
elif position.amount > 0 and \
price > cost_basis * (1 + context.PROFIT_TARGET):
profit = (price * position.amount) - (cost_basis * position.amount)
log.info('closing position, taking profit: {}'.format(profit))
order_target_percent(
asset=context.asset,
target=0,
limit_price=price * (1 - context.SLIPPAGE_ALLOWED),
)
else:
log.info('no buy or sell opportunity found')
else:
is_buy = True
if is_buy:
if buy_increment is None:
return
if price * buy_increment > cash:
log.info('not enough base currency to consider buying')
return
log.info('buying position cheaper than cost basis {} < {}'.format(
price, cost_basis))
limit_price = price * (1 + context.SLIPPAGE_ALLOWED)
order(asset=context.asset,
amount=buy_increment,
limit_price=limit_price)
pass
def handle_data(context, data):
current_time = get_datetime().time()
if current_time.hour == 0 and current_time.minute == 0:
print('Current date is ' + str(get_datetime().date()))
context.i += 1
if context.i < context.atr_time:
return
if not data.can_trade(context.asset):
return
starting_cash = context.portfolio.starting_cash
current = data.current(context.asset, 'close')
price = data.current(context.asset, 'price')
last_price = data.history(context.asset,
'price',
bar_count=context.atr_time - 1,
frequency=context.tf)
if order_vol == 0.1 and stop == 0.9975:
if context.i % 60 == 0:
ohlcv_data = data.history(
context.asset,
fields=['open', 'high', 'low', 'close', 'volume'],
bar_count=1,
frequency='H')
get_ohlcv(database=db,
exchange=exchange_name,
pair='BTCUSDT',
open=ohlcv_data.open,
high=ohlcv_data.high,
low=ohlcv_data.low,
close=ohlcv_data.close,
volume=ohlcv_data.volume,
timestamp=datetime.timestamp(get_datetime()))
bb_data = data.history(context.asset,
'close',
bar_count=context.bb,
frequency=context.tf)
hlc_data = data.history(context.asset,
fields=['high', 'low', 'close'],
bar_count=context.atr_time,
frequency=context.tf)
upperband, middleband, lowerband = talib.BBANDS(bb_data,
timeperiod=context.bb - 1,
nbdevup=2,
nbdevdn=2,
matype=0)
upperband, middleband, lowerband = upperband[-1], middleband[
-1], lowerband[-1]
bb_range = upperband - lowerband
record(price=price,
starting_cash=starting_cash,
cash=context.portfolio.cash,
upperband=upperband,
middleband=middleband,
lowerband=lowerband,
num_trades=context.num_trades,
order_result=context.order_result)
context.model = pyrenko.renko()
optimal_brick = context.model.set_brick_size(HLC_history=hlc_data)
context.model.build_history(prices=last_price)
prev_dir = context.model.get_renko_directions()
last_dir = prev_dir[-4:-1]
if not context.is_open:
if last_dir == context.open_trigger and bb_range > 500:
order_target_percent(context.asset,
order_vol,
limit_price=current * 1.001)
context.is_open = True
context.started = get_open_orders(context.asset)[-1].dt
context.order_price = get_open_orders(context.asset)[-1].limit
context.amount = get_open_orders(context.asset)[-1].amount
positions(db,
type=algo_type,
side='Buy',
start=context.started,
open_price=context.order_price,
finish=None,
close=None,
amount=context.amount,
status='Open',
closed_by=None,
exchange=exchange_name,
timestamp=datetime.timestamp(get_datetime()))
else:
if current <= context.order_price * stop and stop != 0:
close_id = order_target_percent(context.asset,
0,
limit_price=current)
context.is_open = False
context.num_trades += 1
price_diff = current - context.order_price
context.order_result.append(price_diff)
context.finished = get_order(close_id).dt
context.close_price = get_order(close_id).limit
context.closed_by = 'Stop Loss'
record(num_trades=context.num_trades,
order_result=context.order_result)
query = Position.select(fn.MAX(Position.id))
p = (Position.update({
'finished': context.finished,
'closed_price': context.close_price,
'closed_by': context.closed_by,
'status': 'Closed'
}).where(Position.id == query.scalar()))
p.execute()
else:
if last_dir == context.close_trigger:
close_id = order_target_percent(context.asset,
0,
limit_price=current)
context.model = pyrenko.renko()
context.is_open = False
price_diff = current - context.order_price
context.order_result.append(price_diff)
context.num_trades += 1
context.finished = get_order(close_id).dt
context.close_price = get_order(close_id).limit
context.closed_by = 'Algo'
record(num_trades=context.num_trades,
order_result=context.order_result)
query = Position.select(fn.MAX(Position.id))
p = (Position.update({
'finished': context.finished,
'closed_price': context.close_price,
'closed_by': context.closed_by,
'status': 'Closed'
}).where(Position.id == query.scalar()))
p.execute(db)
def handle_data(context, data):
# define the windows for the moving averages
short_window = 50
long_window = 200
# Skip as many bars as long_window to properly compute the average
context.i += 1
if context.i < long_window:
return
# Compute moving averages calling data.history() for each
# moving average with the appropriate parameters. We choose to use
# minute bars for this simulation -> freq="1m"
# Returns a pandas dataframe.
short_data = data.history(context.asset,
'price',
bar_count=short_window,
frequency="1T",
)
short_mavg = short_data.mean()
long_data = data.history(context.asset,
'price',
bar_count=long_window,
frequency="1T",
)
long_mavg = long_data.mean()
# Let's keep the price of our asset in a more handy variable
price = data.current(context.asset, 'price')
# If base_price is not set, we use the current value. This is the
# price at the first bar which we reference to calculate price_change.
if context.base_price is None:
context.base_price = price
price_change = (price - context.base_price) / context.base_price
# Save values for later inspection
record(price=price,
cash=context.portfolio.cash,
price_change=price_change,
short_mavg=short_mavg,
long_mavg=long_mavg)
# Since we are using limit orders, some orders may not execute immediately
# we wait until all orders are executed before considering more trades.
orders = get_open_orders(context.asset)
if len(orders) > 0:
return
# Exit if we cannot trade
if not data.can_trade(context.asset):
return
# We check what's our position on our portfolio and trade accordingly
pos_amount = context.portfolio.positions[context.asset].amount
# Trading logic
if short_mavg > long_mavg and pos_amount == 0:
# we buy 100% of our portfolio for this asset
order_target_percent(context.asset, 1)
elif short_mavg < long_mavg and pos_amount > 0:
# we sell all our positions for this asset
order_target_percent(context.asset, 0)
def handle_data(context, data):
log.info('handling bar {}'.format(data.current_dt))
buying_price = data.current(
context.trading_pairs[context.buying_exchange], 'price')
log.info('price on buying exchange {exchange}: {price}'.format(
exchange=context.buying_exchange.name.upper(),
price=buying_price,
))
selling_price = data.current(
context.trading_pairs[context.selling_exchange], 'price')
log.info('price on selling exchange {exchange}: {price}'.format(
exchange=context.selling_exchange.name.upper(),
price=selling_price,
))
# If for example,
# selling price = 50
# buying price = 25
# expected gap = 1
# If follows that,
# selling price - buying price / buying price
# 50 - 25 / 25 = 1
gap = (selling_price - buying_price) / buying_price
log.info(
'the price gap: {gap} ({gap_percent}%)'.format(
gap=gap,
gap_percent=gap * 100
)
)
record(buying_price=buying_price, selling_price=selling_price, gap=gap)
# Waiting for orders to close before initiating new ones
for exchange in context.trading_pairs:
asset = context.trading_pairs[exchange]
orders = get_open_orders(asset)
if orders:
log.info(
'found {order_count} open orders on {exchange_name} '
'skipping bar until all open orders execute'.format(
order_count=len(orders),
exchange_name=exchange.name
)
)
return
# Consider the least ambitious entry point first
# Override of wider gap is found
entry_points = sorted(
context.entry_points,
key=lambda point: point['gap'],
)
buy_amount = None
for entry_point in entry_points:
if gap > entry_point['gap']:
buy_amount = entry_point['amount']
if buy_amount:
log.info('found buy trigger for amount: {}'.format(buy_amount))
place_orders(
context=context,
amount=buy_amount,
buying_price=buying_price,
selling_price=selling_price,
action='enter'
)
else:
# Consider the narrowest exit gap first
# Override of wider gap is found
exit_points = sorted(
context.exit_points,
key=lambda point: point['gap'],
reverse=True
)
sell_amount = None
for exit_point in exit_points:
if gap < exit_point['gap']:
sell_amount = exit_point['amount']
if sell_amount:
log.info('found sell trigger for amount: {}'.format(sell_amount))
place_orders(
context=context,
amount=sell_amount,
buying_price=buying_price,
selling_price=selling_price,
action='exit'
)
def handle_data(context, data):
# This handle_data function is where the real work is done. Our data is
# minute-level tick data, and each minute is called a frame. This function
# runs on each frame of the data.
# We flag the first period of each day.
# Since cryptocurrencies trade 24/7 the `before_trading_starts` handle
# would only execute once. This method works with minute and daily
# frequencies.
today = data.current_dt.floor('1D')
if today != context.current_day:
context.traded_today = False
context.current_day = today
# We're computing the volume-weighted-average-price of the security
# defined above, in the context.market variable. For this example, we're
# using three bars on the 15 min bars.
# The frequency attribute determine the bar size. We use this convention
# for the frequency alias:
# http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
prices = data.history(context.market,
fields='close',
bar_count=50,
frequency=context.CANDLE_SIZE)
# Ta-lib calculates various technical indicator based on price and
# volume arrays.
# In this example, we are comp
rsi = talib.RSI(prices.values, timeperiod=14)
# We need a variable for the current price of the security to compare to
# the average. Since we are requesting two fields, data.current()
# returns a DataFrame with
current = data.current(context.market, fields=['close', 'volume'])
price = current['close']
# If base_price is not set, we use the current value. This is the
# price at the first bar which we reference to calculate price_change.
if context.base_price is None:
context.base_price = price
price_change = (price - context.base_price) / context.base_price
cash = context.portfolio.cash
# Now that we've collected all current data for this frame, we use
# the record() method to save it. This data will be available as
# a parameter of the analyze() function for further analysis.
record(volume=current['volume'],
price=price,
price_change=price_change,
rsi=rsi[-1],
cash=cash)
# We are trying to avoid over-trading by limiting our trades to
# one per day.
if context.traded_today:
return
# TODO: retest with open orders
# Since we are using limit orders, some orders may not execute immediately
# we wait until all orders are executed before considering more trades.
orders = get_open_orders(context.market)
if len(orders) > 0:
log.info('exiting because orders are open: {}'.format(orders))
return
# Exit if we cannot trade
if not data.can_trade(context.market):
return
# Another powerful built-in feature of the Catalyst backtester is the
# portfolio object. The portfolio object tracks your positions, cash,
# cost basis of specific holdings, and more. In this line, we calculate
# how long or short our position is at this minute.
pos_amount = context.portfolio.positions[context.market].amount
if rsi[-1] <= context.RSI_OVERSOLD and pos_amount == 0:
log.info('{}: buying - price: {}, rsi: {}'.format(
data.current_dt, price, rsi[-1]))
# Set a style for limit orders,
limit_price = price * 1.005
order_target_percent(context.market, 1, limit_price=limit_price)
context.traded_today = True
elif rsi[-1] >= context.RSI_OVERBOUGHT and pos_amount > 0:
log.info('{}: selling - price: {}, rsi: {}'.format(
data.current_dt, price, rsi[-1]))
limit_price = price * 0.995
order_target_percent(context.market, 0, limit_price=limit_price)
context.traded_today = True
def handle_data(context, data):
# This handle_data function is where the real work is done. Our data is
# minute-level tick data, and each minute is called a frame. This function
# runs on each frame of the data.
# We flag the first period of each day.
# Since cryptocurrencies trade 24/7 the `before_trading_starts` handle
# would only execute once. This method works with minute and daily
# frequencies.
today = data.current_dt.floor('1D')
if today != context.current_day:
context.traded_today = False
context.current_day = today
# We're computing the volume-weighted-average-price of the security
# defined above, in the context.market variable. For this example, we're
# using three bars on the 15 min bars.
# The frequency attribute determine the bar size. We use this convention
# for the frequency alias:
# http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
prices = data.history(
context.market,
fields='close',
bar_count=50,
frequency=context.CANDLE_SIZE
)
# Ta-lib calculates various technical indicator based on price and
# volume arrays.
# In this example, we are comp
rsi = talib.RSI(prices.values, timeperiod=14)
# We need a variable for the current price of the security to compare to
# the average. Since we are requesting two fields, data.current()
# returns a DataFrame with
current = data.current(context.market, fields=['close', 'volume'])
price = current['close']
# If base_price is not set, we use the current value. This is the
# price at the first bar which we reference to calculate price_change.
if context.base_price is None:
context.base_price = price
price_change = (price - context.base_price) / context.base_price
cash = context.portfolio.cash
# Now that we've collected all current data for this frame, we use
# the record() method to save it. This data will be available as
# a parameter of the analyze() function for further analysis.
record(
volume=current['volume'],
price=price,
price_change=price_change,
rsi=rsi[-1],
cash=cash
)
# We are trying to avoid over-trading by limiting our trades to
# one per day.
if context.traded_today:
return
# TODO: retest with open orders
# Since we are using limit orders, some orders may not execute immediately
# we wait until all orders are executed before considering more trades.
orders = get_open_orders(context.market)
if len(orders) > 0:
log.info('exiting because orders are open: {}'.format(orders))
return
# Exit if we cannot trade
if not data.can_trade(context.market):
return
# Another powerful built-in feature of the Catalyst backtester is the
# portfolio object. The portfolio object tracks your positions, cash,
# cost basis of specific holdings, and more. In this line, we calculate
# how long or short our position is at this minute.
pos_amount = context.portfolio.positions[context.market].amount
if rsi[-1] <= context.RSI_OVERSOLD and pos_amount == 0:
log.info(
'{}: buying - price: {}, rsi: {}'.format(
data.current_dt, price, rsi[-1]
)
)
# Set a style for limit orders,
limit_price = price * 1.005
order_target_percent(
context.market, 1, limit_price=limit_price
)
context.traded_today = True
elif rsi[-1] >= context.RSI_OVERBOUGHT and pos_amount > 0:
log.info(
'{}: selling - price: {}, rsi: {}'.format(
data.current_dt, price, rsi[-1]
)
)
limit_price = price * 0.995
order_target_percent(
context.market, 0, limit_price=limit_price
)
context.traded_today = True
def handle_data(context, data):
# This handle_data function is where the real work is done. Our data is
# minute-level tick data, and each minute is called a frame. This function
# runs on each frame of the data.
# We flag the first period of each day.
# Since cryptocurrencies trade 24/7 the `before_trading_starts` handle
# would only execute once. This method works with minute and daily
# frequencies.
today = data.current_dt.floor('1D')
if today != context.current_day:
context.traded_today = False
context.current_day = today
# We're computing the volume-weighted-average-price of the security
# defined above, in the context.eth_btc variable. For this example, we're
# using three bars on the 15 min bars.
# The frequency attribute determine the bar size. We use this convention
# for the frequency alias:
# http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
prices = data.history(context.eth_btc,
fields='close',
bar_count=50,
frequency='15T')
# Ta-lib calculates various technical indicator based on price and
# volume arrays.
# In this example, we are comp
rsi = talib.RSI(prices.values, timeperiod=14)
upper, middle, lower = talib.BBANDS(prices.values,
timeperiod=20,
nbdevup=2,
nbdevdn=2,
matype=MA_Type.EMA)
# We need a variable for the current price of the security to compare to
# the average. Since we are requesting two fields, data.current()
# returns a DataFrame with
current = data.current(context.eth_btc, fields=['close', 'volume'])
price = current['close']
# If base_price is not set, we use the current value. This is the
# price at the first bar which we reference to calculate price_change.
if context.base_price is None:
context.base_price = price
price_change = (price - context.base_price) / context.base_price
cash = context.portfolio.cash
# Now that we've collected all current data for this frame, we use
# the record() method to save it. This data will be available as
# a parameter of the analyze() function for further analysis.
record(price=price,
volume=current['volume'],
upper_band=upper[-1],
lower_band=lower[-1],
price_change=price_change,
rsi=rsi[-1],
cash=cash)
# We are trying to avoid over-trading by limiting our trades to
# one per day.
if context.traded_today:
return
# Since we are using limit orders, some orders may not execute immediately
# we wait until all orders are executed before considering more trades.
orders = get_open_orders(context.eth_btc)
if len(orders) > 0:
return
# Exit if we cannot trade
if not data.can_trade(context.eth_btc):
return
# Another powerful built-in feature of the Catalyst backtester is the
# portfolio object. The portfolio object tracks your positions, cash,
# cost basis of specific holdings, and more. In this line, we calculate
# how long or short our position is at this minute.
pos_amount = context.portfolio.positions[context.eth_btc].amount
# In this example, we're using a trigger instead of buying directly after
# a signal. Since this is mean reversion, our signals go against the
# momentum. Using a trigger allow us to spot the opportunity but trade
# only when a trade reversal begins.
if context.trigger is not None:
# The tread_direction() method determines the trend based on the last
# two bars of the series.
direction = trend_direction(rsi)
if context.trigger[1] == 'buy' and direction == 'up':
log.info('{}: buying - price: {}, rsi: {}, bband: {}'.format(
data.current_dt, price, rsi[-1], lower[-1]))
order_target_percent(context.eth_btc, 1)
context.traded_today = True
context.trigger = None
elif context.trigger[1] == 'sell' and direction == 'down':
log.info('{}: selling - price: {}, rsi: {}, bband: {}'.format(
data.current_dt, price, rsi[-1], upper[-1]))
order_target_percent(context.eth_btc, 0)
context.traded_today = True
context.trigger = None
# If we found a signal but no trade reversal within two hours, we
# reset the trigger.
elif context.trigger[0] + timedelta(hours=2) < data.current_dt:
context.trigger = None
else:
# Determining the entry and exit signals based on RSI and SMA
if rsi[-1] <= 30 and pos_amount == 0:
context.trigger = (data.current_dt, 'buy')
elif rsi[-1] >= 80 and pos_amount > 0:
context.trigger = (data.current_dt, 'sell')
def handle_data(context, data):
log.info('handling bar {}'.format(data.current_dt))
buying_price = data.current(
context.trading_pairs[context.buying_exchange], 'price')
log.info('price on buying exchange {exchange}: {price}'.format(
exchange=context.buying_exchange.name.upper(),
price=buying_price,
))
selling_price = data.current(
context.trading_pairs[context.selling_exchange], 'price')
log.info('price on selling exchange {exchange}: {price}'.format(
exchange=context.selling_exchange.name.upper(),
price=selling_price,
))
# If for example,
# selling price = 50
# buying price = 25
# expected gap = 1
# If follows that,
# selling price - buying price / buying price
# 50 - 25 / 25 = 1
gap = (selling_price - buying_price) / buying_price
log.info(
'the price gap: {gap} ({gap_percent}%)'.format(
gap=gap,
gap_percent=gap * 100
)
)
record(buying_price=buying_price, selling_price=selling_price, gap=gap)
# Waiting for orders to close before initiating new ones
for exchange in context.trading_pairs:
asset = context.trading_pairs[exchange]
orders = get_open_orders(asset)
if orders:
log.info(
'found {order_count} open orders on {exchange_name} '
'skipping bar until all open orders execute'.format(
order_count=len(orders),
exchange_name=exchange.name
)
)
return
# Consider the least ambitious entry point first
# Override of wider gap is found
entry_points = sorted(
context.entry_points,
key=lambda point: point['gap'],
)
buy_amount = None
for entry_point in entry_points:
if gap > entry_point['gap']:
buy_amount = entry_point['amount']
if buy_amount:
log.info('found buy trigger for amount: {}'.format(buy_amount))
place_orders(
context=context,
amount=buy_amount,
buying_price=buying_price,
selling_price=selling_price,
action='enter'
)
else:
# Consider the narrowest exit gap first
# Override of wider gap is found
exit_points = sorted(
context.exit_points,
key=lambda point: point['gap'],
reverse=True
)
sell_amount = None
for exit_point in exit_points:
if gap < exit_point['gap']:
sell_amount = exit_point['amount']
if sell_amount:
log.info('found sell trigger for amount: {}'.format(sell_amount))
place_orders(
context=context,
amount=sell_amount,
buying_price=buying_price,
selling_price=selling_price,
action='exit'
)
