def condition(data): # Define the condition function of the Decision
if redis_client.active_rising_peak(
) and data.btc_balance > 0: # We carry out the condition check only if the decision is set active in redis
band = fetch()
if band: # Since the data is in redis the band is active
if data.sell < band[
'lower']: # If the sell-price has fallen below the band we must take action
return True
delta = round2(
data.sell * UPPER_LIMIT_FACTOR - band['upper']
) # Change in upper band based on the current sell price
if delta > 0: # If the sell price has increased such that the band is pushed upwards we update the band (it NEVER goes down)
band['upper'] += delta
band['lower'] += delta
log("\n" + current_time())
log("Delta = ${}. Pushing band up to: ${} - ${}.".format(
delta, band['lower'], band['upper']))
push(band)
btc = client.btc()
client.cancel_all_orders()
client.sell_order(btc, round2(band['upper']))
else: # The band is inactive
if data.sell > ACTIVATION_THRESHOLD: # Avg Sell is above Activation Threshold so we activate the band
log("\n" + current_time())
log(
"Sell price = ${} has risen above the Activation Threshold = ${}"
.format(data.sell, ACTIVATION_THRESHOLD), True)
b = {
'lower': data.sell * LOWER_LIMIT_FACTOR,
'upper': data.sell * UPPER_LIMIT_FACTOR
} # Set band values
rds.set(
REDIS_KEY, json.dumps(b)
) # Convert dictionary to json string and store it in redis server
btc = client.btc()
client.cancel_all_orders()
client.sell_order(
btc, round2(b['upper'])
) # Set up a sell order for the upper band limit
return False
def condition(data): # Define the condition function of the Decision
if redis_client.active_falling_trench() and data.usd_balance > 10:
band = fetch()
if band: # Since the data is in redis the band is active
if data.buy > band['upper']: # If the buy-price has risen above the band we must take action (acquire BTC)
return True
delta = round2(band['lower'] - data.buy * LOWER_LIMIT_FACTOR) # Change in lower band based on the current buy price
if delta > 0: # If the buy price has decreased such that the band is pushed downwards we update the band (it NEVER goes up)
band['upper'] -= delta
band['lower'] -= delta
if band['upper'] > 1.005 * data.buy: # Ensure that the upper band is not more than 0.5% above the current buy price
band['upper'] = 1.005 * data.buy
log("Calculated upper limit > 1.005 times buy price. Adjusting.")
log(current_time())
log("Delta = -${}. Pushing band down to: ${} - ${}.\n".format(delta, band['lower'], band['upper']))
push(band)
usd = data.usd_balance
client.cancel_all_orders()
client.usd_buy_order(usd, round2(band['lower']))
log("New buy order created.")
else: # The band is inactive
if data.buy < ACTIVATION_THRESHOLD: # Buy Price is below Activation Threshold so we activate the band
log(current_time())
log("Avg Buy price = ${} has fallen below the Activation Threshold = ${}".format(data.avg_buy, ACTIVATION_THRESHOLD), True)
b = {'upper': ACTIVATION_THRESHOLD, 'lower': data.buy * LOWER_LIMIT_FACTOR} # Set band values
rds.set(REDIS_KEY, json.dumps(b)) # Convert dictionary to json string and store it in redis server
usd = data.usd_balance
client.cancel_all_orders()
client.usd_buy_order(usd, round2(b['lower'])) # Set up a sell order for the upper band limit
log("Creating band: ${} - ${}".format(b['lower'], b['upper']))
return False
def condition(data): # Define the condition function of the Decision
if redis_client.active_rising_peak() and data.btc_balance > 0: # We carry out the condition check only if the decision is set active in redis
band = fetch()
if band: # Since the data is in redis the band is active
if data.sell < band['lower']: # If the sell-price has fallen below the band we must take action
return True
delta = round2(data.sell * UPPER_LIMIT_FACTOR - band['upper']) # Change in upper band based on the current sell price
if delta > 0: # If the sell price has increased such that the band is pushed upwards we update the band (it NEVER goes down)
band['upper'] += delta
band['lower'] += delta
log("\n" + current_time())
log("Delta = ${}. Pushing band up to: ${} - ${}.".format(delta, band['lower'], band['upper']))
push(band)
btc = client.btc()
client.cancel_all_orders()
client.sell_order(btc, round2(band['upper']))
else: # The band is inactive
if data.sell > ACTIVATION_THRESHOLD: # Avg Sell is above Activation Threshold so we activate the band
log("\n" + current_time())
log("Sell price = ${} has risen above the Activation Threshold = ${}".format(data.sell, ACTIVATION_THRESHOLD), True)
b = {'lower': data.sell * LOWER_LIMIT_FACTOR, 'upper': data.sell * UPPER_LIMIT_FACTOR} # Set band values
rds.set(REDIS_KEY, json.dumps(b)) # Convert dictionary to json string and store it in redis server
btc = client.btc()
client.cancel_all_orders()
client.sell_order(btc, round2(b['upper'])) # Set up a sell order for the upper band limit
return False
def condition(data): # Define the condition function of the Decision
if data.btc_balance > 0:
if data.sell < MINIMUM_THRESHOLD:
log(current_time())
log("Sell price = ${} has fallen below the Min. Threshold = ${}".format(data.sell, MINIMUM_THRESHOLD), True)
return True
return False
def condition(data):
if redis_client.active_minimize_loss() and data.btc_balance > 0:
if data.sell < DROP_FACTOR * data.last_buy_price:
log(current_time())
log("Original Buy Price: {obuy} - Current Sell Price: {sell} - Delta: {delta} - %age: {pct}".format(obuy=data.last_buy_price, sell=data.sell, delta=data.sell - data.last_buy_price, pct=(data.sell - data.last_buy_price)/data.last_buy_price * 100))
return True
return False
def condition(data): # Define the condition function of the Decision
if data.btc_balance > 0:
if data.sell < MINIMUM_THRESHOLD:
log(current_time())
log(
"Sell price = ${} has fallen below the Min. Threshold = ${}".
format(data.sell, MINIMUM_THRESHOLD), True)
return True
return False
def condition(data):
if redis_client.active_minimum_profit() and data.btc_balance > 0:
if BAND_LOWER * data.last_buy_price < data.sell < BAND_UPPER * data.last_buy_price:
if max_price(data.weighted_sell_prices) > TRIGGER_THRESHOLD * data.last_buy_price: # The weighted sell prices exceeded the upper threshold before dropping sometime in the past
log(current_time())
log("Orig. Buy Price: {obuy} - Curr. Sell Price: {sell} - Delta: {delta} - %age: {pct}".format(obuy=data.last_buy_price, sell=data.sell, delta=data.sell - data.last_buy_price, pct=(data.sell - data.last_buy_price) / data.last_buy_price * 100))
log("Max Weighted Sell price: {}".format(max_price(data.sell_prices)))
return True
return False
def condition(data):
if redis_client.active_minimum_profit() and data.btc_balance > 0:
if BAND_LOWER * data.last_buy_price < data.sell < BAND_UPPER * data.last_buy_price:
if max_price(
data.weighted_sell_prices
) > TRIGGER_THRESHOLD * data.last_buy_price: # The weighted sell prices exceeded the upper threshold before dropping sometime in the past
log(current_time())
log("Orig. Buy Price: {obuy} - Curr. Sell Price: {sell} - Delta: {delta} - %age: {pct}"
.format(obuy=data.last_buy_price,
sell=data.sell,
delta=data.sell - data.last_buy_price,
pct=(data.sell - data.last_buy_price) /
data.last_buy_price * 100))
log("Max Weighted Sell price: {}".format(
max_price(data.sell_prices)))
return True
return False