ACE X CA X Lead Capital 量化課程
· 4 min read
Quant Lead & BD & MM
Details
此頁的ＱＲ連結
Details
課綱
Crypto-Arsenal
- 註冊連結 (可以 Google 註冊)
- 如何用 CA 串接和自動化任何
TradingView策略: 到 Strategy >New Strategy>TradingView(詳細教學) - 如何用 CA 回測和實單策略: 到 Strategy >
New Strategy>Technical Indicator>EMA
TradingView
7/26 簡易EMA策略 + 簡易 Filter (濾網) i.e RSI & Momentum
//@version=5
strategy(title="CA_test", shorttitle="EMA", overlay=true, default_qty_value = 80)
ema10 = ta.ema(close,10)
ema20 = ta.ema(close,20)
//filter
rsi = ta.rsi(close,20)
mom = math.abs( ta.mom(close,49) )
//condition
long = ta.crossover(ema10,ema20)
short = ta.crossunder(ema10,ema20)
//position
if(long and mom >= 10 and rsi < 50)
if(strategy.position_size != 0)
strategy.close_all()
strategy.entry("long",strategy.long)
if(short and mom >= 10 and rsi > 50)
if(strategy.position_size != 0)
strategy.close_all()
strategy.entry("short",strategy.short)
if( rsi > 80 or rsi < 20 )
strategy.close_all()
plot(rsi , color = color.blue)
plot(ema10, color=#f37f20)
plot(ema20 , color = color.red)
7/19 簡易EMA策略
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © Crypto-Arsenal
//@version=5
strategy("我的策略", overlay=true, margin_long=100, margin_short=100)
//@version=5
//indicator(title="Moving Average Exponential", shorttitle="EMA", overlay=true, timeframe="", timeframe_gaps=true)
ema10 = ta.ema(close,10)
ema20 = ta.ema(close,20)
plot(ema10, title="Smoothing Line", color=#f37f20)
plot(ema20, title="Smoothing Line", color=#4320f3)
longCondition = ta.crossover(ema10,ema20)
if (longCondition)
strategy.close_all()
strategy.entry("My Long Entry Id", strategy.long)
shortCondition = ta.crossunder(ema10,ema20)
if (shortCondition)
strategy.close_all()
strategy.entry("My Short Entry Id", strategy.short)
Python
7/19 Superposition EMA Python 策略
"""
Contributed by BCA_Daniel @BCA_Quantrade / Crypto-Arsenal & Alan Wu @Crypto-Arsenal
-
Superposition EMAs are designed to smooth out EMAs and reduce noise interference.
This strategy utilizes four EMAs and uses the output of the previous EMA as the input for the next EMA.
Strategy designer: BCA_Daniel 2023.07.16
"""
class Strategy(StrategyBase):
def __init__(self):
# strategy property
self.subscribed_books = {}
self.period = 60 * 30
self.options = {}
self.last_type = 'sell'
### fast / slow ema period
self.fast_period = 14
self.sp1_period = 14
self.sp2_period =14
self.slow_period = 14
self.divide_quote = 0
self.proportion = 0.2
def on_order_state_change(self, order):
pass
# called every self.period
def trade(self, candles):
exchange, pair, base, quote = CA.get_exchange_pair()
ca_position = self.get_ca_position()
close_price_history = [candle['close'] for candle in candles[exchange][pair]]
high_price_history = [candle['high'] for candle in candles[exchange][pair]]
low_price_history = [candle['low'] for candle in candles[exchange][pair]]
# convert to chronological order for talib
close_price_history.reverse()
high_price_history.reverse()
low_price_history.reverse()
# convert np.array
close_price_history = np.array(close_price_history)
high_price_history = np.array(high_price_history)
low_price_history = np.array(low_price_history)
close_price = close_price_history[0]
high_price = high_price_history[0]
#ema fast / slow create
#np.nan_to_num used to refilled nan to 0 for output array
EMA_fast = talib.EMA(close_price_history, timeperiod=self.fast_period)
EMA_fast_filled = np.nan_to_num(EMA_fast, nan=0)
EMA_superposition_1 = talib.EMA(EMA_fast_filled, timeperiod=self.sp1_period)
EMA_superposition_1_filled = np.nan_to_num(EMA_superposition_1, nan=0)
EMA_superposition_2 = talib.EMA(EMA_superposition_1_filled,self.sp2_period)
EMA_superposition_2_filled = np.nan_to_num(EMA_superposition_2, nan=0)
EMA_slow = talib.EMA(EMA_superposition_2_filled, self.slow_period)
if len(close_price_history) < self.slow_period + 1:
return []
# current ema fast / slow
curr_ema_fast = EMA_fast[-1]
curr_ema_slow = EMA_slow[-1]
# previous time stamp ema
prev_ema_fast = EMA_fast[-2]
prev_ema_slow = EMA_slow[-2]
# get available balance
base_balance = CA.get_balance(exchange, base)
quote_balance = CA.get_balance(exchange, quote)
available_base_amount = base_balance.available
available_quote_amount = quote_balance.available
if self.divide_quote == 0:
self.divide_quote = np.round(available_quote_amount* self.proportion, 5)
# initialize signal to be 0
signal = 0
if available_base_amount< self.divide_quote/high_price and available_base_amount > -self.divide_quote/high_price:
# open long position
if curr_ema_fast > curr_ema_slow and prev_ema_fast < prev_ema_slow:
signal = 1
# open short position
if curr_ema_fast < curr_ema_slow and prev_ema_fast > prev_ema_slow:
signal = -1
# Sell short
if signal == -1:
self['is_shorting'] = 'true'
CA.log('Sell short ' + str(base))
if ca_position:
CA.place_order(exchange, pair, action='close_long', conditional_order_type='OTO', child_conditional_orders=[{'action': 'open_short', 'percent':80}])
else:
CA.place_order(exchange, pair, action='open_short', percent=80)
# Buy long
if signal == 1:
self['is_shorting'] = 'false'
CA.log('Buy ' + str(base))
if ca_position:
CA.place_order(exchange, pair, action='close_short', conditional_order_type='OTO', child_conditional_orders=[{'action': 'open_long', 'percent':80}])
else:
CA.place_order(exchange, pair, action='open_long', percent=80)
# return current total position: -n 0, +n where n is number of contracts
def get_ca_position(self):
exchange, pair, base, quote = CA.get_exchange_pair()
long_position = CA.get_position(exchange, pair, CA.PositionSide.LONG)
if long_position:
return abs(long_position.total_size)
short_position = CA.get_position(exchange, pair, CA.PositionSide.SHORT)
if short_position:
return -1 * abs(short_position.total_size)
return 0