Name
基于均线的趋势追踪策略-Trend-Following-Strategy-Based-on-Moving-Averages
Author
ChaoZhang
Strategy Description
该策略是基于均线的趋势追踪策略。它利用不同周期的EMA均线构建多组交易信号,实现了趋势追踪交易。当价格跌破较长周期的均线时逐步加仓做多,实现了价格平均成本的降低。策略同时设定止损条件,在短周期均线转折时止损退出,锁定收益。
该策略使用5条不同周期的EMA均线构建交易信号。分别是10日线、20日线、50日线、100日线和200日线。策略根据价格与这些均线的关系设定4组买入条件,实现金字塔加仓。
当价格低于20日线且高于50日线时,触发第一组买入;当价格低于50日线且高于100日线时,触发第二组买入;当价格低于100日线且高于200日线时,触发第三组买入;当价格低于200日线时,触发第四组买入。每次买入的数量也在逐步放大,分别是qt1、qt2、qt3和qt4。
在卖出方面,策略同时使用了两组止损条件。第一组是当价格高于10日线,且10日线高于其他均线时止损;第二组是当价格低于前一日的10日线,且10日线高于其他均线时止损。这两组条件可以有效锁定中间的短线利润。
该策略最大的优势在于可以自动跟踪市场趋势实现长线持有。通过多组买入条件和分批加仓,可以不断降低买入成本,获得超额收益。同时,也规避了单一买入点带来的价格风险。
在止损方面,策略也进行了优化设计。向上追踪短周期均线的turning point,可以快速止损并锁定收益。这避免了亏损进一步扩大的风险。
该策略面临的最大风险是被困于长线调整行情中。当大盘处于震荡或下行通道时,均线信号并不可靠。这时有可能持续买入并持有头寸,承受较大亏损。
另一个风险点是均线并不总是精确。价格短期内的跳空或扩张行情可能会让均线发出错误信号。这需要结合其它技术指标进行验证和优化。
可以考虑在买入条件中增加其它技术指标判断,例如成交量指标、Bollinger Bands信号等。这可以进一步提高买入的成功率。
在卖出条件中也可以加入Boll上轨或关键支撑位作为第二层止损线。这可以减少不必要的小止损。或者加入移动止损功能实时调整止损线,也可以进一步锁定收益。
该策略使用均线系统进行趋势跟踪交易。通过金字塔式分批加仓,可以最大程度获取趋势行情的收益。同时也设定了双重止损条件保护了资金安全。这是一个值得长期跟踪并实盘验证的策略。可以根据实际情况不断对参数和模型进行优化调整。
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This strategy is a trend following strategy based on moving averages. It utilizes EMA lines of different periods to construct multiple sets of trading signals for trend tracking. When the price breaks below longer period moving averages, the strategy will progressively build long positions to lower the average cost. The strategy also sets stop loss conditions based on short period moving average turns to secure profits.
The strategy employs 5 EMA lines of different periods for constructing trading signals, which are 10-day, 20-day, 50-day, 100-day and 200-day EMA. The strategy defines 4 buying conditions based on the price relationship with these EMA lines to implement pyramid trading.
When the price is below 20-day EMA while above 50-day EMA, the first buy signal is triggered. When below 50-day EMA while above 100-day EMA, the second buy signal is triggered. The third and fourth buy signals are triggered when the price drops below 100-day EMA and 200-day EMA respectively. The position size also expands progressively from qt1 to qt4.
On the sell side, there are two groups of stop loss conditions. The first is to stop loss when price surpasses 10-day EMA while 10-day EMA is above other EMA lines. The second is similar but it exits when price drops below previous close of 10-day EMA. These two conditions are to secure short-term profits during trends.
The biggest advantage of this strategy is the ability to automatically track market trends for long-term holds. By utilizing multiple entry conditions and progressive position building, it constantly reduces cost basis to yield excess returns. It also diversifies away the pricing risk associated with a single entry price level.
On the stop loss side, the strategy tracks short period moving average turning points to quickly take profit and avoid further losses. This minimizes the downside risk.
The biggest risk this strategy faces is being stuck in long lasting consolidations or downtrends. When the overall market enters a ranging or downward channel, moving average signals become less reliable. This could lead to sustained losses from continued long builds.
Another risk point is that moving averages do not always pinpoint turns accurately. Price gaps or explosive moves could result in faulty signals. This calls for additional technical indicators for verification and optimization.
Other technical indicators like volume or Bollinger Bands could be incorporated into the buying conditions to further improve entry accuracy.
The second layers of stop loss based on Bollinger Upper Band or key support areas could also be added. This helps avoid unnecessary small stops. Implementing adaptive stop loss to trail prices is another enhancement area to better protect profits.
This strategy implements trend following trading via a moving average system. Through pyramid position building, it aims to maximize returns from sustained trends while securing capital preservation with dual stop loss mechanisms. This is a strategy worthy of further tracking and live testing. Parameters and models can be incrementally optimized based on practical performance.
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Strategy Arguments
| Argument | Default | Description |
|---|---|---|
| v_input_int_1 | 5 | Quantity 1 |
| v_input_int_2 | 10 | Quantity 2 |
| v_input_int_3 | 15 | Quantity 3 |
| v_input_int_4 | 20 | Quantity 4 |
| v_input_1 | true | Profit Percentage |
| v_input_int_5 | 2 | Pyramiding |
Source (PineScript)
/*backtest
start: 2023-01-11 00:00:00
end: 2024-01-17 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
//@version=5
strategy("EMA_zorba1", shorttitle="zorba_ema", overlay=true)
// Input parameters
qt1 = input.int(5, title="Quantity 1", minval=1)
qt2 = input.int(10, title="Quantity 2", minval=1)
qt3 = input.int(15, title="Quantity 3", minval=1)
qt4 = input.int(20, title="Quantity 4", minval=1)
ema10 = ta.ema(close, 10)
ema20 = ta.ema(close, 20)
ema50 = ta.ema(close, 50)
ema100 = ta.ema(close, 100)
ema200 = ta.ema(close, 200)
// Date range filter
start_date = timestamp(year=2021, month=1, day=1)
end_date = timestamp(year=2024, month=10, day=27)
in_date_range = true
// Profit condition
profit_percentage = input(1, title="Profit Percentage") // Adjust this value as needed
// Pyramiding setting
pyramiding = input.int(2, title="Pyramiding", minval=1, maxval=10)
// Buy conditions
buy_condition_1 = in_date_range and close < ema20 and close > ema50 and close < open and close < low[1]
buy_condition_2 = in_date_range and close < ema50 and close > ema100 and close < open and close < low[1]
buy_condition_3 = in_date_range and close < ema100 and close > ema200 and close < open and close < low[1]
buy_condition_4 = in_date_range and close < ema200 and close < open and close < low[1]
// Exit conditions
profit_condition = strategy.position_avg_price * (1 + profit_percentage / 100) <= close
exit_condition_1 = in_date_range and (close > ema10 and ema10 > ema20 and ema10 > ema50 and ema10 > ema100 and ema10 > ema200 and close < open) and profit_condition and close < low[1] and close < low[2]
exit_condition_2 = in_date_range and (close < ema10 and close[1] > ema10 and close < close[1] and ema10 > ema20 and ema10 > ema50 and ema10 > ema100 and ema10 > ema200 and close < open) and profit_condition and close < low[1] and close < low[2]
// Exit condition for when today's close is less than the previous day's low
//exit_condition_3 = close < low[1]
// Strategy logic
strategy.entry("Buy1", strategy.long, qty=qt1 * pyramiding, when=buy_condition_1)
strategy.entry("Buy2", strategy.long, qty=qt2 * pyramiding, when=buy_condition_2)
strategy.entry("Buy3", strategy.long, qty=qt3 * pyramiding, when=buy_condition_3)
strategy.entry("Buy4", strategy.long, qty=qt4 * pyramiding, when=buy_condition_4)
strategy.close("Buy1", when=exit_condition_1 or exit_condition_2)
strategy.close("Buy2", when=exit_condition_1 or exit_condition_2)
strategy.close("Buy3", when=exit_condition_1 or exit_condition_2)
strategy.close("Buy4", when=exit_condition_1 or exit_condition_2)
Detail
https://www.fmz.com/strategy/439203
Last Modified
2024-01-18 12:23:59