Name
双向震荡突破策略Monthly-Returns-Strategy-with-Benchmark
Author
ChaoZhang
Strategy Description
本策略是一个基于双向价格震荡突破的量化交易策略。它使用Pivot高低点作为价格关键支撑阻力位,并在价格突破Pivot高点的时候做多,突破Pivot低点的时候做空,实现双向操作。
该策略的核心逻辑基于双向价格的关键点突破。具体来说,有以下几个步骤:
-
计算指定周期的Pivot高低点。这里使用ta.pivothigh()和ta.pivotlow()函数计算出最近2日内最高价作为高点,最近1日内最低价作为低点。
-
当价格突破上述计算出的关键高点时,做多入场。当价格突破关键低点时,做空入场。
-
使用止损单止损。做多时,止损价为高点+最小价格变动单位;做空时,止损价为低点-最小价格变动单位。
-
绘制关键的高低点,便于直观判断。
这样,当价格出现震荡的时候,能够在关键点突破的时候及时入场,并快速止损,从而获利。当价格不断突破新高或新低时,策略可以实现多次累积盈利。
这种双向突破策略主要有以下几个优势:
-
简单易懂,容易实施。该策略仅仅依靠突破Pivot高低点来入场,非常简单。
-
容易设置止损。做多做空分别以高点低点+最小变动距离作为止损位,可以快速止损,有效控制风险。
-
能够双向操作。无论行情看涨看跌,该策略都可以顺势而为,累积盈利。
-
适合震荡行情。当价格出现频繁的上涨下跌时,策略可以频繁进入场内获得利润。
尽管该策略有上述优点,但也存在一些风险需要注意:
-
关键点确定不当可能增加亏损。如果关键高低点设置不当,极端情况下可能追高杀跌。
-
震荡结束后可能开始亏损。当价格开始出现单边突破而不是震荡时,该策略则很难获利。
-
突破可能是短期的假突破。短期内也可能出现假突破,使得策略产生失误交易。
总的来说,该策略更适合在震荡行情中使用。投资者需要注意判断行情,避免在趋势行情中使用本策略。同时要注意防范关键点确定错误、假突破等问题造成的损失。
考虑到上述风险,该策略的优化空间主要在以下几个方面:
-
智能选择关键高低点参数。可以通过机器学习等手段,让系统自动优化选择比较合适的关键点参数。
-
结合趋势判断。在策略基础上增加对趋势的判断逻辑,在震荡行情中使用该策略,在单边趋势中关闭策略,从而减少损失。
-
增加止损策略。可以设计更精细的止损策略,如移动止损、区间突破止损等,进一步控制风险。
本策略是一个简单实用的双向突破策略。它依靠价格关键点的突破来入场,并设置了止损保证风险可控。该策略适合震荡行情,可以双向交易获得盈利。但该策略也存在一定的风险,投资者需要谨慎使用,优化的空间在于智能选择参数、结合趋势判断和设计更精细的止损策略等方面。
||
This is a quantitative trading strategy based on monthly returns display. It uses Pine Script to calculate and present monthly and yearly returns of the strategy, as well as benchmark returns, in a table on the chart for analysis.
The core logic of this strategy is to track and calculate monthly and yearly performance, and display it in a table format. The key steps are:
-
Calculate monthly and yearly return based on change in equity.
-
Calculate benchmark monthly and yearly returns based on price change.
-
Store the monthly and yearly returns in arrays.
-
When bar is confirmed, populate a table using the stored return arrays to present monthly perf.
-
Display benchmark in second table row. Display alpha in third row.
By doing so, this script can clearly present the monthly returns in an organized table, along with benchmark comparison. This helps analyze performance.
The main advantages of this monthly return strategy are:
-
Intuitive display of monthly returns. The table format makes performance easy to analyze.
-
Clear benchmark comparison. Displaying a separate benchmark row allows strategy vs market performance analysis.
-
Alpha calculation. The alpha row shows if strategy is outperforming/underperforming the benchmark.
-
Customizable parameters for flexibility. User can set colors, date range, benchmark symbol etc as needed.
Some risks to note with this strategy are:
-
No trading logic. This merely displays returns, does not include actual trades.
-
Historical performance may not continue. As with any backtest, past returns do not guarantee future performance.
-
Potential errors in return calculation. Bugs could lead to incorrect monthly return figures.
Overall this script mainly serves as a performance visualization tool. The risks can be mitigated by ensuring accuracy of return calculations and not relying solely on backtests.
Some ways this monthly return strategy could be improved are:
-
Add actual trading strategy whose performance is displayed. Combine with a quant strategy.
-
Add further benchmark customization parameters like benchmark symbol, timeframe etc.
-
Enhance table formatting for better visuals - colors, cells, formatting etc.
-
Add other return metrics - CAGR, Sharpe ratio etc for more analysis.
This is a strategy focused specifically on displaying monthly returns of system and benchmark in table format for easier analysis. Its advantages are intuitive visualization and comparison of strategy vs benchmark. Risks are lack of trading logic and reliance on backtest. It can be enhanced by combining with quant strategy, adding further customization options and more metrics.
[/trans]
Strategy Arguments
| Argument | Default | Description |
|---|---|---|
| v_input_1 | 2 | (?Pivot Points)leftBars |
| v_input_2 | true | rightBars |
| v_input_3 | 2 | (?Monthly Table)Return Precision |
| v_input_4 | timestamp(01 Jan 2000 00:00 +0000) | From Date |
| v_input_color_1 | green | Gradient Colors |
| v_input_color_2 | red | loss_color |
| v_input_bool_1 | true | (?Benchmark)Use current Symbol for Benchmark |
| v_input_5 | BTC_USDT:swap | Benchmark |
| v_input_bool_2 | true | Display Benchmark? |
| v_input_bool_3 | true | Display Alpha? |
Source (PineScript)
/*backtest
start: 2023-12-01 00:00:00
end: 2023-12-31 23:59:59
period: 2h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
//@version=5
strategy('Monthly Returns with Benchmark', overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=25, commission_type=strategy.commission.percent, commission_value=0.1)
////////////
// Inputs //
// Pivot points inputs
leftBars = input(2, group = "Pivot Points")
rightBars = input(1, group = "Pivot Points")
// Styling inputs
prec = input(2, title='Return Precision', group = "Monthly Table")
from_date = input(timestamp("01 Jan 2000 00:00 +0000"), "From Date", group = "Monthly Table")
prof_color = input.color(color.green, title = "Gradient Colors", group = "Monthly Table", inline = "colors")
loss_color = input.color(color.red, title = "", group = "Monthly Table", inline = "colors")
// Benchmark inputs
use_cur = input.bool(true, title = "Use current Symbol for Benchmark", group = "Benchmark")
symb_bench = input('BTC_USDT:swap', title = "Benchmark", group = "Benchmark")
disp_bench = input.bool(true, title = "Display Benchmark?", group = "Benchmark")
disp_alpha = input.bool(true, title = "Display Alpha?", group = "Benchmark")
// Pivot Points Strategy
swh = ta.pivothigh(leftBars, rightBars)
swl = ta.pivotlow(leftBars, rightBars)
hprice = 0.0
hprice := not na(swh) ? swh : hprice[1]
lprice = 0.0
lprice := not na(swl) ? swl : lprice[1]
le = false
le := not na(swh) ? true : le[1] and high > hprice ? false : le[1]
se = false
se := not na(swl) ? true : se[1] and low < lprice ? false : se[1]
if le
strategy.entry('PivRevLE', strategy.long, comment='PivRevLE', stop=hprice + syminfo.mintick)
if se
strategy.entry('PivRevSE', strategy.short, comment='PivRevSE', stop=lprice - syminfo.mintick)
plot(hprice, color=color.new(color.green, 0), linewidth=2)
plot(lprice, color=color.new(color.red, 0), linewidth=2)
///////////////////
// MONTHLY TABLE //
new_month = month(time) != month(time[1])
new_year = year(time) != year(time[1])
eq = strategy.equity
bench_eq = close
// benchmark eq
bench_eq_htf = request.security(symb_bench, timeframe.period, close)
if (not use_cur)
bench_eq := bench_eq_htf
bar_pnl = eq / eq[1] - 1
bench_pnl = bench_eq / bench_eq[1] - 1
cur_month_pnl = 0.0
cur_year_pnl = 0.0
// Current Monthly P&L
cur_month_pnl := bar_index == 0 ? 0 :
time >= from_date and (time[1] < from_date or new_month) ? bar_pnl :
(1 + cur_month_pnl[1]) * (1 + bar_pnl) - 1
// Current Yearly P&L
cur_year_pnl := bar_index == 0 ? 0 :
time >= from_date and (time[1] < from_date or new_year) ? bar_pnl :
(1 + cur_year_pnl[1]) * (1 + bar_pnl) - 1
bench_cur_month_pnl = 0.0
bench_cur_year_pnl = 0.0
// Current Monthly P&L - Bench
bench_cur_month_pnl := bar_index == 0 or (time[1] < from_date and time >= from_date) ? 0 :
time >= from_date and new_month ? bench_pnl :
(1 + bench_cur_month_pnl[1]) * (1 + bench_pnl) - 1
// Current Yearly P&L - Bench
bench_cur_year_pnl := bar_index == 0 ? 0 :
time >= from_date and (time[1] < from_date or new_year) ? bench_pnl :
(1 + bench_cur_year_pnl[1]) * (1 + bench_pnl) - 1
var month_time = array.new_int(0)
var year_time = array.new_int(0)
var month_pnl = array.new_float(0)
var year_pnl = array.new_float(0)
var bench_month_pnl = array.new_float(0)
var bench_year_pnl = array.new_float(0)
// Filling monthly / yearly pnl arrays
if array.size(month_time) > 0
if month(time) == month(array.get(month_time, array.size(month_time) - 1))
array.pop(month_pnl)
array.pop(bench_month_pnl)
array.pop(month_time)
if array.size(year_time) > 0
if year(time) == year(array.get(year_time, array.size(year_time) - 1))
array.pop(year_pnl)
array.pop(bench_year_pnl)
array.pop(year_time)
if (time >= from_date)
array.push(month_time, time)
array.push(year_time, time)
array.push(month_pnl, cur_month_pnl)
array.push(year_pnl, cur_year_pnl)
array.push(bench_year_pnl, bench_cur_year_pnl)
array.push(bench_month_pnl, bench_cur_month_pnl)
// Monthly P&L Table
var monthly_table = table(na)
if array.size(year_pnl) > 0 and barstate.islastconfirmedhistory
monthly_table := table.new(position.bottom_right, columns=15, rows=array.size(year_pnl) * 3 + 5, border_width=1)
// Fill monthly performance
table.cell(monthly_table, 0, 0, 'Perf', bgcolor = #999999)
table.cell(monthly_table, 1, 0, 'Jan', bgcolor = #999999)
table.cell(monthly_table, 2, 0, 'Feb', bgcolor = #999999)
table.cell(monthly_table, 3, 0, 'Mar', bgcolor = #999999)
table.cell(monthly_table, 4, 0, 'Apr', bgcolor = #999999)
table.cell(monthly_table, 5, 0, 'May', bgcolor = #999999)
table.cell(monthly_table, 6, 0, 'Jun', bgcolor = #999999)
table.cell(monthly_table, 7, 0, 'Jul', bgcolor = #999999)
table.cell(monthly_table, 8, 0, 'Aug', bgcolor = #999999)
table.cell(monthly_table, 9, 0, 'Sep', bgcolor = #999999)
table.cell(monthly_table, 10, 0, 'Oct', bgcolor = #999999)
table.cell(monthly_table, 11, 0, 'Nov', bgcolor = #999999)
table.cell(monthly_table, 12, 0, 'Dec', bgcolor = #999999)
table.cell(monthly_table, 13, 0, ' ', bgcolor = #999999)
table.cell(monthly_table, 14, 0, 'Year', bgcolor = #999999)
max_abs_y = math.max(math.abs(array.max(year_pnl)), math.abs(array.min(year_pnl)))
max_abs_m = math.max(math.abs(array.max(month_pnl)), math.abs(array.min(month_pnl)))
for yi = 0 to array.size(year_pnl) - 1 by 1
table.cell(monthly_table, 0, yi + 1, str.tostring(year(array.get(year_time, yi))), bgcolor=#cccccc)
table.cell(monthly_table, 13, yi + 1, ' ', bgcolor=#999999)
y_color = color.from_gradient(array.get(year_pnl, yi), -max_abs_y, max_abs_y, loss_color, prof_color)
table.cell(monthly_table, 14, yi + 1, str.tostring(math.round(array.get(year_pnl, yi) * 100, prec)), bgcolor=y_color)
for mi = 0 to array.size(month_time) - 1 by 1
m_row = year(array.get(month_time, mi)) - year(array.get(year_time, 0)) + 1
m_col = month(array.get(month_time, mi))
m_color = color.from_gradient(array.get(month_pnl, mi), -max_abs_m, max_abs_m, loss_color, prof_color)
table.cell(monthly_table, m_col, m_row, str.tostring(math.round(array.get(month_pnl, mi) * 100, prec)), bgcolor=m_color)
// Fill benchmark performance
next_row = array.size(year_pnl) + 1
if (disp_bench)
table.cell(monthly_table, 0, next_row, 'Bench', bgcolor=#999999)
table.cell(monthly_table, 1, next_row, 'Jan', bgcolor=#999999)
table.cell(monthly_table, 2, next_row, 'Feb', bgcolor=#999999)
table.cell(monthly_table, 3, next_row, 'Mar', bgcolor=#999999)
table.cell(monthly_table, 4, next_row, 'Apr', bgcolor=#999999)
table.cell(monthly_table, 5, next_row, 'May', bgcolor=#999999)
table.cell(monthly_table, 6, next_row, 'Jun', bgcolor=#999999)
table.cell(monthly_table, 7, next_row, 'Jul', bgcolor=#999999)
table.cell(monthly_table, 8, next_row, 'Aug', bgcolor=#999999)
table.cell(monthly_table, 9, next_row, 'Sep', bgcolor=#999999)
table.cell(monthly_table, 10, next_row, 'Oct', bgcolor=#999999)
table.cell(monthly_table, 11, next_row, 'Nov', bgcolor=#999999)
table.cell(monthly_table, 12, next_row, 'Dec', bgcolor=#999999)
table.cell(monthly_table, 13, next_row, ' ', bgcolor = #999999)
table.cell(monthly_table, 14, next_row, 'Year', bgcolor=#999999)
max_bench_abs_y = math.max(math.abs(array.max(bench_year_pnl)), math.abs(array.min(bench_year_pnl)))
max_bench_abs_m = math.max(math.abs(array.max(bench_month_pnl)), math.abs(array.min(bench_month_pnl)))
for yi = 0 to array.size(year_time) - 1 by 1
table.cell(monthly_table, 0, yi + 1 + next_row + 1, str.tostring(year(array.get(year_time, yi))), bgcolor=#cccccc)
table.cell(monthly_table, 13, yi + 1 + next_row + 1, ' ', bgcolor=#999999)
y_color = color.from_gradient(array.get(bench_year_pnl, yi), -max_bench_abs_y, max_bench_abs_y, loss_color, prof_color)
table.cell(monthly_table, 14, yi + 1 + next_row + 1, str.tostring(math.round(array.get(bench_year_pnl, yi) * 100, prec)), bgcolor=y_color)
for mi = 0 to array.size(month_time) - 1 by 1
m_row = year(array.get(month_time, mi)) - year(array.get(year_time, 0)) + 1
m_col = month(array.get(month_time, mi))
m_color = color.from_gradient(array.get(bench_month_pnl, mi), -max_bench_abs_m, max_bench_abs_m, loss_color, prof_color)
table.cell(monthly_table, m_col, m_row + next_row + 1, str.tostring(math.round(array.get(bench_month_pnl, mi) * 100, prec)), bgcolor=m_color)
// Fill Alpha
if (disp_alpha)
next_row := array.size(year_pnl) * 2 + 3
table.cell(monthly_table, 0, next_row, 'Alpha', bgcolor=#999999)
table.cell(monthly_table, 1, next_row, 'Jan', bgcolor=#999999)
table.cell(monthly_table, 2, next_row, 'Feb', bgcolor=#999999)
table.cell(monthly_table, 3, next_row, 'Mar', bgcolor=#999999)
table.cell(monthly_table, 4, next_row, 'Apr', bgcolor=#999999)
table.cell(monthly_table, 5, next_row, 'May', bgcolor=#999999)
table.cell(monthly_table, 6, next_row, 'Jun', bgcolor=#999999)
table.cell(monthly_table, 7, next_row, 'Jul', bgcolor=#999999)
table.cell(monthly_table, 8, next_row, 'Aug', bgcolor=#999999)
table.cell(monthly_table, 9, next_row, 'Sep', bgcolor=#999999)
table.cell(monthly_table, 10, next_row, 'Oct', bgcolor=#999999)
table.cell(monthly_table, 11, next_row, 'Nov', bgcolor=#999999)
table.cell(monthly_table, 12, next_row, 'Dec', bgcolor=#999999)
table.cell(monthly_table, 13, next_row, '', bgcolor=#999999)
table.cell(monthly_table, 14, next_row, 'Year', bgcolor=#999999)
max_alpha_abs_y = 0.0
for yi = 0 to array.size(year_time) - 1 by 1
if (math.abs(array.get(year_pnl, yi) - array.get(bench_year_pnl, yi)) > max_alpha_abs_y)
max_alpha_abs_y := math.abs(array.get(year_pnl, yi) - array.get(bench_year_pnl, yi))
max_alpha_abs_m = 0.0
for mi = 0 to array.size(month_pnl) - 1 by 1
if (math.abs(array.get(month_pnl, mi) - array.get(bench_month_pnl, mi)) > max_alpha_abs_m)
max_alpha_abs_m := math.abs(array.get(month_pnl, mi) - array.get(bench_month_pnl, mi))
for yi = 0 to array.size(year_time) - 1 by 1
table.cell(monthly_table, 0, yi + 1 + next_row + 1, str.tostring(year(array.get(year_time, yi))), bgcolor=#cccccc)
table.cell(monthly_table, 13, yi + 1 + next_row + 1, ' ', bgcolor=#999999)
y_color = color.from_gradient(array.get(year_pnl, yi) - array.get(bench_year_pnl, yi), -max_alpha_abs_y, max_alpha_abs_y, loss_color, prof_color)
table.cell(monthly_table, 14, yi + 1 + next_row + 1, str.tostring(math.round((array.get(year_pnl, yi) - array.get(bench_year_pnl, yi)) * 100, prec)), bgcolor=y_color)
for mi = 0 to array.size(month_time) - 1 by 1
m_row = year(array.get(month_time, mi)) - year(array.get(year_time, 0)) + 1
m_col = month(array.get(month_time, mi))
m_color = color.from_gradient(array.get(month_pnl, mi) - array.get(bench_month_pnl, mi), -max_alpha_abs_m, max_alpha_abs_m, loss_color, prof_color)
table.cell(monthly_table, m_col, m_row + next_row + 1, str.tostring(math.round((array.get(month_pnl, mi) - array.get(bench_month_pnl, mi)) * 100, prec)), bgcolor=m_color)
Detail
https://www.fmz.com/strategy/440464
Last Modified
2024-01-30 17:40:05