OBV Pyramid Strategy Based on Coinrule Script

This strategy is called "OBV Pyramid". It designs opening positions based on the OBV indicator and adopts a pyramid increasing position approach to track trends for profit after they emerge.

Pine ScriptTrend following EMA SMA OBV

Author: ChaoZhang
Complexity: Starter · 86 lines
Parameters: 6
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy is called "OBV Pyramid". It designs opening positions based on the OBV indicator and adopts a pyramid increasing position approach to track trends for profit after they emerge.

Principles

This strategy uses the OBV indicator to determine the trend direction. The OBV indicator judges price trends based on changes in trading volume, as shifts in volume reflect market participant attitudes. When the OBV line crosses above 0, it indicates strengthening buying power and an uptrend forming. When crossing below 0, it signals strengthening selling pressure and a downtrend.

This strategy confirms an uptrend by the OBV crossing above 0. When an uptrend forms, pyramid increasing position rules are set, allowing up to 7 additional buys. It aims to profit from the trend while setting take profit and stop loss exists.

Advantage Analysis

The biggest advantage of this strategy is catching trends using the pyramid approach to track trends and profit from them. Also, solid risk control is in place with take profit and stop loss settings.

Specifically, the main advantages are:

Accurate trend judgement using OBV;
Pyramid buying to track trends for profit;
Take profit/stop loss controlling risk;
Simple and clear logic.

Risk Analysis

The main risks come from two aspects:

Inaccurate OBV signals leading to missed opportunities or wrong entries;
Too many additional buys enlarging risk.

Solutions:

Optimize OBV parameters to ensure accuracy;
Reasonably limit additional buys for controllable risk.

Optimization Directions

Main optimization directions:

OBV parameter optimization for higher accuracy;
Optimization of number of additional buys and amounts;
Take profit/stop loss optimization;
Incorporating other indicators to avoid sole OBV reliance.

This can make the strategy more stable, controllable and extensible.

Conclusion

Overall this is a very practical strategy. It uses OBV to determine trend direction, then pyramids into the trend for profit. The logic is simple and clear for easy backtesting. It has applicability value and with further parameter, risk and money management optimization, performance can improve further, warranting additional research.

Parameters

Name	Default	What it controls
v_input_1	0	Strategy Direction: long \| short \| all
v_input_2	250	Fast filter length
v_input_3	500	Slow filter length
v_input_4	20	LengthOBV
v_input_5	3	ProfitTarget_Percent
v_input_6	10	LossTarget_Percent

Source Pine Script

/*backtest
start: 2023-11-07 00:00:00
end: 2023-12-07 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © RafaelZioni

//@version=4

strategy(title = " OBV Pyr", overlay = true, pyramiding=5,initial_capital = 10000, default_qty_type= strategy.percent_of_equity, default_qty_value = 20, calc_on_order_fills=false, slippage=0,commission_type=strategy.commission.percent,commission_value=0.075)
strat_dir_input = input(title="Strategy Direction", defval="long", options=["long", "short", "all"])
strat_dir_value = strat_dir_input == "long" ? strategy.direction.long : strat_dir_input == "short" ? strategy.direction.short : strategy.direction.all
strategy.risk.allow_entry_in(strat_dir_value)

//
fastLength = input(250, title="Fast filter length ", minval=1)
slowLength = input(500,title="Slow filter length",  minval=1)
source=close
v1=ema(source,fastLength)
v2=ema(source,slowLength)
 
//
 
filter=true 
src = close


LengthOBV = input(20)

nv = change(src) > 0 ? volume : change(src) < 0 ? -volume : 0*volume 
c = cum(nv) 
c_tb = c - sma(c,LengthOBV) 

// Conditions

longCond = crossover(c_tb,0)
//shortCond =crossunder(cnv_tb,0)

//

longsignal  = (v1 > v2 or filter == false ) and longCond
//shortsignal = (v1 < v2 or filter == false ) and shortCond 
 
//set take profit
 
ProfitTarget_Percent = input(3)
Profit_Ticks = close * (ProfitTarget_Percent / 100) / syminfo.mintick
 
//set take profit
 
LossTarget_Percent = input(10)
Loss_Ticks = close * (LossTarget_Percent / 100) / syminfo.mintick
 
 
////Order Placing
//
strategy.entry("Entry 1", strategy.long, when=strategy.opentrades == 0 and longsignal)
//
strategy.entry("Entry 2", strategy.long, when=strategy.opentrades == 1 and longsignal)
//
strategy.entry("Entry 3", strategy.long, when=strategy.opentrades == 2 and longsignal)
//
strategy.entry("Entry 4", strategy.long, when=strategy.opentrades == 3 and longsignal)
//
strategy.entry("Entry 5", strategy.long, when=strategy.opentrades == 4 and longsignal)
//
strategy.entry("Entry 6", strategy.long, when=strategy.opentrades == 5 and longsignal)
//
strategy.entry("Entry 7", strategy.long, when=strategy.opentrades == 6 and longsignal)
//
//
//
if strategy.position_size > 0
    strategy.exit(id="Exit 1", from_entry="Entry 1", profit=Profit_Ticks, loss=Loss_Ticks)
    strategy.exit(id="Exit 2", from_entry="Entry 2", profit=Profit_Ticks, loss=Loss_Ticks)
    strategy.exit(id="Exit 3", from_entry="Entry 3", profit=Profit_Ticks, loss=Loss_Ticks)
    strategy.exit(id="Exit 4", from_entry="Entry 4", profit=Profit_Ticks, loss=Loss_Ticks)
    strategy.exit(id="Exit 5", from_entry="Entry 5", profit=Profit_Ticks, loss=Loss_Ticks)
    strategy.exit(id="Exit 6", from_entry="Entry 6", profit=Profit_Ticks, loss=Loss_Ticks)
    strategy.exit(id="Exit 7", from_entry="Entry 7", profit=Profit_Ticks, loss=Loss_Ticks)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-12-08. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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