Just a rough simulation, so that everyone has a specific concept of the amount of lost margins. You can download the notebook and upload it to the FMZ research environment, and run the code yourself.
Binance's risk estimation of selling short over rise and buying long over fall trend strategies
First look at the original report: https://www.fmz.com/digest-topic/5584 and the improved report: https://www.fmz.com/digest-topic/5588
The strategy has been public sharing for 4 days now. The early stage preformed very well, with high returns and few retracements, so that many users are using a very high leverage to gamble a 10% return per day. However, as stated in the initial report, there is no perfect strategy. Selling short over rise and buying long over fall trend make use of the characteristics of altcoins to rise and fall together. If a currency moves out of a unique trend, it will accumulate many holding positions. although a moving average was used to track the initial price, the risks still exist. This report mainly quantifies the specific risks and why the parameter recommended trade_value accounts for 3% of the total funds.
In order to highlight the code, we put in advanced of this part, everyone should try first run the following code (starting from the import libraries part).
In order to simulate, we assume there are 20 currencies, but only need to add BTC and ETH, and use BTC to represent 19 currencies with constant prices. ETH represents the currency with independent trend currency. Due to it is only a simulation, there is no need to track the initial price by moving average here, assuming that the price is rising at a rapid rate.
First, simulate the situation where the price of a single currency continues to rise. Stop_loss indicates that the stop loss deviates. Here is only a simulation. The actual situation will have intermittent retracement, it will not as bad as the simulation.
Suppose there is no retracement to this currency, when the stop loss deviation is 0.41, ETH has risen 44% at this time, and the results finally became lost 7 times of the trading value, that is, trade_value * 7. If trade_value is set to 3% of total funds, then loss = total funds * 0.03 * 7. The maximum retracement is about 0.03 * 7 = 21%.
You can estimate your own risk tolerance based on the results below.
btc_price = [1]*500 # Bitcoin price, always unchanged
eth_price = [i/100. for i in range(100,500)] # Ethereum, up 1% in one cycle
for stop_loss in [i/1000. for i in range(10,1500,50)]:
e = Exchange(['BTC','ETH'],initial_balance=10000,commission=0.0005,log=False)
trade_value = 300 # 300 transactions
for i in range(200):
index = (btc_price[i]*19+eth_price[i])/20. # index
e.Update(i,{'BTC':btc_price[i], 'ETH':eth_price[i]})
diff_btc = btc_price[i] - index # deviation
diff_eth = eth_price[i] - index
btc_value = e.account['BTC']['value']*np.sign(e.account['BTC']['amount'])
eth_value = e.account['ETH']['value']*np.sign(e.account['ETH']['amount'])
aim_btc_value = -trade_value*round(diff_btc/0.01,1)*19 # Here BTC replaces 19 currencies
aim_eth_value = -trade_value*round(diff_eth/0.01,1)
if aim_btc_value - btc_value > 20:
e.Buy('BTC',btc_price[i],(aim_btc_value - btc_value)/btc_price[i])
if aim_eth_value - eth_value < -20 and diff_eth < stop_loss:
e.Sell('ETH',eth_price[i], (eth_value-aim_eth_value)/eth_price[i],diff_eth)
if diff_eth > stop_loss and eth_value < 0: # Stop loss
stop_price = eth_price[i]
e.Buy('ETH',eth_price[i], (-eth_value)/eth_price[i],diff_eth)
print('Currency price：',stop_price,' Stop loss deviation：', stop_loss,'Final balance：',e.df['total'].iloc[-1], ' Multiple of losing trade volume：',round((e.initial_balance-e.df['total'].iloc[-1])/300,1))
Currency price： 1.02 Stop loss deviation： 0.01 Final balance： 9968.840396 Multiple of losing trade volume： 0.1 Currency price： 1.07 Stop loss deviation： 0.06 Final balance： 9912.862738 Multiple of losing trade volume： 0.3 Currency price： 1.12 Stop loss deviation： 0.11 Final balance： 9793.616067 Multiple of losing trade volume： 0.7 Currency price： 1.17 Stop loss deviation： 0.16 Final balance： 9617.477263 Multiple of losing trade volume： 1.3 Currency price： 1.23 Stop loss deviation： 0.21 Final balance： 9337.527299 Multiple of losing trade volume： 2.2 Currency price： 1.28 Stop loss deviation： 0.26 Final balance： 9051.5166 Multiple of losing trade volume： 3.2 Currency price： 1.33 Stop loss deviation： 0.31 Final balance： 8721.285267 Multiple of losing trade volume： 4.3 Currency price： 1.38 Stop loss deviation： 0.36 Final balance： 8350.582251 Multiple of losing trade volume： 5.5 Currency price： 1.44 Stop loss deviation： 0.41 Final balance： 7856.720861 Multiple of losing trade volume： 7.1 Currency price： 1.49 Stop loss deviation： 0.46 Final balance： 7406.412066 Multiple of losing trade volume： 8.6 Currency price： 1.54 Stop loss deviation： 0.51 Final balance： 6923.898356 Multiple of losing trade volume： 10.3 Currency price： 1.59 Stop loss deviation： 0.56 Final balance： 6411.276143 Multiple of losing trade volume： 12.0 Currency price： 1.65 Stop loss deviation： 0.61 Final balance： 5758.736222 Multiple of losing trade volume： 14.1 Currency price： 1.7 Stop loss deviation： 0.66 Final balance： 5186.230956 Multiple of losing trade volume： 16.0 Currency price： 1.75 Stop loss deviation： 0.71 Final balance： 4588.802975 Multiple of losing trade volume： 18.0 Currency price： 1.81 Stop loss deviation： 0.76 Final balance： 3841.792751 Multiple of losing trade volume： 20.5 Currency price： 1.86 Stop loss deviation： 0.81 Final balance： 3193.215479 Multiple of losing trade volume： 22.7 Currency price： 1.91 Stop loss deviation： 0.86 Final balance： 2525.155765 Multiple of losing trade volume： 24.9 Currency price： 1.96 Stop loss deviation： 0.91 Final balance： 1837.699982 Multiple of losing trade volume： 27.2 Currency price： 2.02 Stop loss deviation： 0.96 Final balance： 988.009942 Multiple of losing trade volume： 30.0 Currency price： 2.07 Stop loss deviation： 1.01 Final balance： 260.639618 Multiple of losing trade volume： 32.5 Currency price： 2.12 Stop loss deviation： 1.06 Final balance： -483.509646 Multiple of losing trade volume： 34.9 Currency price： 2.17 Stop loss deviation： 1.11 Final balance： -1243.486107 Multiple of losing trade volume： 37.5 Currency price： 2.24 Stop loss deviation： 1.16 Final balance： -2175.438384 Multiple of losing trade volume： 40.6 Currency price： 2.28 Stop loss deviation： 1.21 Final balance： -2968.19255 Multiple of losing trade volume： 43.2 Currency price： 2.33 Stop loss deviation： 1.26 Final balance： -3774.613275 Multiple of losing trade volume： 45.9 Currency price： 2.38 Stop loss deviation： 1.31 Final balance： -4594.305499 Multiple of losing trade volume： 48.6 Currency price： 2.44 Stop loss deviation： 1.36 Final balance： -5594.651063 Multiple of losing trade volume： 52.0 Currency price： 2.49 Stop loss deviation： 1.41 Final balance： -6441.474964 Multiple of losing trade volume： 54.8 Currency price： 2.54 Stop loss deviation： 1.46 Final balance： -7299.652662 Multiple of losing trade volume： 57.7
In simulating the situation of continuous decline, the decline is accompanied by a decrease in the value of the contract, so the risk is higher than the rise, and as the price falls, the rate of increase in losses is accelerating. When the stop loss deviation value is -0.31, the currency price drops by 33% at this time, and a loss of 6.5 transactions. If the trade amount trade_value is set to 3% of the total funds, the maximum retracement is about 0.03 * 6.5 = 19.5%.
btc_price = [1]*500 # Bitcoin price, always unchanged
eth_price = [2-i/100. for i in range(100,200)] # Ethereum
for stop_loss in [-i/1000. for i in range(10,1000,50)]:
e = Exchange(['BTC','ETH'],initial_balance=10000,commission=0.0005,log=False)
trade_value = 300 # 300 transactions
for i in range(100):
index = (btc_price[i]*19+eth_price[i])/20. # index
e.Update(i,{'BTC':btc_price[i], 'ETH':eth_price[i]})
diff_btc = btc_price[i] - index # deviation
diff_eth = eth_price[i] - index
btc_value = e.account['BTC']['value']*np.sign(e.account['BTC']['amount'])
eth_value = e.account['ETH']['value']*np.sign(e.account['ETH']['amount'])
aim_btc_value = -trade_value*round(diff_btc/0.01,1)*19 # Here BTC replaces 19 currencies
aim_eth_value = -trade_value*round(diff_eth/0.01,1)
if aim_btc_value - btc_value < -20:
e.Sell('BTC',btc_price[i],-(aim_btc_value - btc_value)/btc_price[i])
if aim_eth_value - eth_value > 20 and diff_eth > stop_loss:
e.Buy('ETH',eth_price[i], -(eth_value-aim_eth_value)/eth_price[i],diff_eth)
if diff_eth < stop_loss and eth_value > 0:
e.Sell('ETH',eth_price[i], (eth_value)/eth_price[i],diff_eth)
stop_price = eth_price[i]
print('Currency price：',round(stop_price,2),' Stop loss deviation：', stop_loss,'Final balance：',e.df['total'].iloc[-1], ' Multiple of losing trade volume：',round((e.initial_balance-e.df['total'].iloc[-1])/300,1))
Currency price： 0.98 Stop loss deviation： -0.01 Final balance： 9983.039091 Multiple of losing trade volume： 0.1 Currency price： 0.93 Stop loss deviation： -0.06 Final balance： 9922.200148 Multiple of losing trade volume： 0.3 Currency price： 0.88 Stop loss deviation： -0.11 Final balance： 9778.899361 Multiple of losing trade volume： 0.7 Currency price： 0.83 Stop loss deviation： -0.16 Final balance： 9545.316075 Multiple of losing trade volume： 1.5 Currency price： 0.77 Stop loss deviation： -0.21 Final balance： 9128.800213 Multiple of losing trade volume： 2.9 Currency price： 0.72 Stop loss deviation： -0.26 Final balance： 8651.260863 Multiple of losing trade volume： 4.5 Currency price： 0.67 Stop loss deviation： -0.31 Final balance： 8037.598952 Multiple of losing trade volume： 6.5 Currency price： 0.62 Stop loss deviation： -0.36 Final balance： 7267.230651 Multiple of losing trade volume： 9.1 Currency price： 0.56 Stop loss deviation： -0.41 Final balance： 6099.457595 Multiple of losing trade volume： 13.0 Currency price： 0.51 Stop loss deviation： -0.46 Final balance： 4881.767442 Multiple of losing trade volume： 17.1 Currency price： 0.46 Stop loss deviation： -0.51 Final balance： 3394.414792 Multiple of losing trade volume： 22.0 Currency price： 0.41 Stop loss deviation： -0.56 Final balance： 1575.135344 Multiple of losing trade volume： 28.1 Currency price： 0.35 Stop loss deviation： -0.61 Final balance： -1168.50508 Multiple of losing trade volume： 37.2 Currency price： 0.29 Stop loss deviation： -0.66 Final balance： -4071.007983 Multiple of losing trade volume： 46.9 Currency price： 0.25 Stop loss deviation： -0.71 Final balance： -7750.361195 Multiple of losing trade volume： 59.2 Currency price： 0.19 Stop loss deviation： -0.76 Final balance： -13618.366286 Multiple of losing trade volume： 78.7 Currency price： 0.14 Stop loss deviation： -0.81 Final balance： -20711.473968 Multiple of losing trade volume： 102.4 Currency price： 0.09 Stop loss deviation： -0.86 Final balance： -31335.965608 Multiple of losing trade volume： 137.8 Currency price： 0.04 Stop loss deviation： -0.91 Final balance： -51163.223715 Multiple of losing trade volume： 203.9 Currency price： 0.04 Stop loss deviation： -0.96 Final balance： -81178.565715 Multiple of losing trade volume： 303.9
# Libraries to import import pandas as pd import requests import matplotlib.pyplot as plt import seaborn as sns import numpy as np %matplotlib inline
price_usdt = pd.read_csv('https://www.fmz.com/upload/asset/20227de6c1d10cb9dd1.csv ', index_col = 0)
price_usdt.index = pd.to_datetime(price_usdt.index)
price_usdt_norm = price_usdt/price_usdt.fillna(method='bfill').iloc[0,]
price_usdt_btc = price_usdt.divide(price_usdt['BTC'],axis=0)
price_usdt_btc_norm = price_usdt_btc/price_usdt_btc.fillna(method='bfill').iloc[0,]
class Exchange:
def __init__(self, trade_symbols, leverage=20, commission=0.00005, initial_balance=10000, log=False):
self.initial_balance = initial_balance # Initial asset
self.commission = commission
self.leverage = leverage
self.trade_symbols = trade_symbols
self.date = ''
self.log = log
self.df = pd.DataFrame(columns=['margin','total','leverage','realised_profit','unrealised_profit'])
self.account = {'USDT':{'realised_profit':0, 'margin':0, 'unrealised_profit':0, 'total':initial_balance, 'leverage':0, 'fee':0}}
for symbol in trade_symbols:
self.account[symbol] = {'amount':0, 'hold_price':0, 'value':0, 'price':0, 'realised_profit':0, 'margin':0, 'unrealised_profit':0,'fee':0}
def Trade(self, symbol, direction, price, amount, msg=''):
if self.date and self.log:
print('%-20s%-5s%-5s%-10.8s%-8.6s %s'%(str(self.date), symbol, 'buy' if direction == 1 else 'sell', price, amount, msg))
cover_amount = 0 if direction*self.account[symbol]['amount'] >=0 else min(abs(self.account[symbol]['amount']), amount)
open_amount = amount - cover_amount
self.account['USDT']['realised_profit'] -= price*amount*self.commission # Minus handling fee
self.account['USDT']['fee'] += price*amount*self.commission
self.account[symbol]['fee'] += price*amount*self.commission
if cover_amount > 0: # close positions first
self.account['USDT']['realised_profit'] += -direction*(price - self.account[symbol]['hold_price'])*cover_amount # profit
self.account['USDT']['margin'] -= cover_amount*self.account[symbol]['hold_price']/self.leverage # Free margin
self.account[symbol]['realised_profit'] += -direction*(price - self.account[symbol]['hold_price'])*cover_amount
self.account[symbol]['amount'] -= -direction*cover_amount
self.account[symbol]['margin'] -= cover_amount*self.account[symbol]['hold_price']/self.leverage
self.account[symbol]['hold_price'] = 0 if self.account[symbol]['amount'] == 0 else self.account[symbol]['hold_price']
if open_amount > 0:
total_cost = self.account[symbol]['hold_price']*direction*self.account[symbol]['amount'] + price*open_amount
total_amount = direction*self.account[symbol]['amount']+open_amount
self.account['USDT']['margin'] += open_amount*price/self.leverage
self.account[symbol]['hold_price'] = total_cost/total_amount
self.account[symbol]['amount'] += direction*open_amount
self.account[symbol]['margin'] += open_amount*price/self.leverage
self.account[symbol]['unrealised_profit'] = (price - self.account[symbol]['hold_price'])*self.account[symbol]['amount']
self.account[symbol]['price'] = price
self.account[symbol]['value'] = abs(self.account[symbol]['amount'])*price
return True
def Buy(self, symbol, price, amount, msg=''):
self.Trade(symbol, 1, price, amount, msg)
def Sell(self, symbol, price, amount, msg=''):
self.Trade(symbol, -1, price, amount, msg)
def Update(self, date, close_price): # Update assets
self.date = date
self.close = close_price
self.account['USDT']['unrealised_profit'] = 0
for symbol in self.trade_symbols:
if np.isnan(close_price[symbol]):
continue
self.account[symbol]['unrealised_profit'] = (close_price[symbol] - self.account[symbol]['hold_price'])*self.account[symbol]['amount']
self.account[symbol]['price'] = close_price[symbol]
self.account[symbol]['value'] = abs(self.account[symbol]['amount'])*close_price[symbol]
self.account['USDT']['unrealised_profit'] += self.account[symbol]['unrealised_profit']
self.account['USDT']['total'] = round(self.account['USDT']['realised_profit'] + self.initial_balance + self.account['USDT']['unrealised_profit'],6)
self.account['USDT']['leverage'] = round(self.account['USDT']['margin']/self.account['USDT']['total'],4)*self.leverage
self.df.loc[self.date] = [self.account['USDT']['margin'],self.account['USDT']['total'],self.account['USDT']['leverage'],self.account['USDT']['realised_profit'],self.account['USDT']['unrealised_profit']]