多次元ゲーム理論トレーディング戦略：市場行動分析と機関流動性最適化手法の融合

/*backtest
start: 2024-08-21 02:40:00
end: 2025-08-03 08:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
args: [["v_input_int_2",5],["v_input_int_3",5]]
*/

//@version=5
strategy("Game Theory Trading Strategy", overlay=true, initial_capital=10000, pyramiding=3, default_qty_type=strategy.percent_of_equity, default_qty_value=10)

// ===========================
// INPUTS - User Settings
// ===========================

// Herd Behavior Parameters
grp1 = "Herd Behavior Analysis"
rsi_length = input.int(14, "RSI Period", group=grp1)
volume_ma_length = input.int(5, "Volume MA Period", group=grp1)
herd_threshold = input.float(2.0, "Herd Behavior Threshold", step=0.1, group=grp1)

// Liquidity Traps
grp2 = "Liquidity Analysis"
liquidity_lookback = input.int(5, "Liquidity Scan Period", group=grp2)

// Institutional Flow
grp3 = "Institutional Flow Detection"
inst_volume_mult = input.float(2.5, "Institutional Volume Multiplier", step=0.1, group=grp3)
inst_ma_length = input.int(5, "Institutional MA Period", group=grp3)

// Nash Equilibrium
grp4 = "Nash Equilibrium"
nash_period = input.int(4, "Nash Equilibrium Period", group=grp4)
nash_deviation = input.float(0.02, "Equilibrium Deviation Rate", step=0.001, group=grp4)

// Risk Management
grp5 = "Risk Management"
use_sl = input.bool(true, "Use Stop Loss", group=grp5)
sl_percent = input.float(2.0, "Stop Loss %", step=0.1, group=grp5)
use_tp = input.bool(true, "Use Take Profit", group=grp5)
tp_percent = input.float(5.0, "Take Profit %", step=0.1, group=grp5)

// ===========================
// CALCULATIONS
// ===========================

// 1. HERD BEHAVIOR DETECTION
// Is RSI at extreme levels?
rsi = ta.rsi(close, rsi_length)
rsi_extreme_high = rsi > 70
rsi_extreme_low = rsi < 30

// Is volume normal?
volume_ma = ta.sma(volume, volume_ma_length)
volume_spike = volume > volume_ma * herd_threshold

// Momentum calculation
momentum = close - close[5]
momentum_ma = ta.sma(momentum, 5)

// Herd behavior score
herd_buying = rsi_extreme_high and volume_spike and momentum > momentum_ma
herd_selling = rsi_extreme_low and volume_spike and momentum < momentum_ma

// 2. LIQUIDITY TRAPS
// Find highest/lowest levels in the last X bars
recent_high = ta.highest(high, liquidity_lookback)
recent_low = ta.lowest(low, liquidity_lookback)

// 心理价位计算（多层级但简单）
round_level_up = close > 100 ? math.round(close / 100) * 100 + 100 : math.round(close / 10) * 10 + 10
round_level_down = close > 100 ? math.round(close / 100) * 100 - 100 : math.round(close / 10) * 10 - 10

// 是否接近心理价位（简单距离判断）
near_psych_up = math.abs(high - round_level_up) / close < 0.01 
near_psych_down = math.abs(low - round_level_down) / close < 0.01 

// 改进的流动性陷阱检测（保持原逻辑，增加心理价位强化）
liquidity_trap_up = high > recent_high[1] and close < recent_high[1] and volume_spike and near_psych_up
liquidity_trap_down = low < recent_low[1] and close > recent_low[1] and volume_spike and near_psych_down

// 3. INSTITUTIONAL FLOW ANALYSIS
// Large volume trades
institutional_volume = volume > volume_ma * inst_volume_mult

// Accumulation/Distribution
ad = ta.cum(((close - low) - (high - close)) / (high - low) * volume)
ad_ma = ta.sma(ad, inst_ma_length)
accumulation = ad > ad_ma and institutional_volume
distribution = ad < ad_ma and institutional_volume

// Smart Money Index-like
smart_money = (close - open) / (high - low) * volume
smart_money_ma = ta.sma(smart_money, 5)
smart_money_positive = smart_money > smart_money_ma

// 4. NASH EQUILIBRIUM LEVELS
// Price equilibrium zones
price_mean = ta.sma(close, nash_period)
price_std = ta.stdev(close, nash_period)
upper_nash = price_mean + price_std * nash_deviation
lower_nash = price_mean - price_std * nash_deviation

// Is price near Nash equilibrium?
near_nash_equilibrium = close > lower_nash and close < upper_nash
above_nash = close > upper_nash
below_nash = close < lower_nash

// 5. GAME THEORY SIGNALS

// Contrarian signals (opposite to the majority)
contrarian_buy = herd_selling and (accumulation or liquidity_trap_down)
contrarian_sell = herd_buying and (distribution or liquidity_trap_up)

// Momentum signals (trend following but with smart money)
momentum_buy = below_nash and smart_money_positive and not herd_buying
momentum_sell = above_nash and not smart_money_positive and not herd_selling

// Nash equilibrium reversion signals
nash_reversion_buy = below_nash and close > close[1] and volume > volume_ma
nash_reversion_sell = above_nash and close < close[1] and volume > volume_ma

// Main signals
long_signal = contrarian_buy or momentum_buy or nash_reversion_buy
short_signal = contrarian_sell or momentum_sell or nash_reversion_sell

// ===========================
// POSITION MANAGEMENT
// ===========================

// Position size (Game theory minimax principle)
position_size = 1.0
if near_nash_equilibrium
    position_size := 0.5  // Lower position size near Nash equilibrium
else if institutional_volume
    position_size := 1.5  // Higher position size with institutional flow

// ===========================
// STRATEGY IMPLEMENTATION
// ===========================

// Long positions
if long_signal and strategy.position_size <= 0
    strategy.entry("GT Long", strategy.long, qty=position_size)
    
    if use_sl
        strategy.exit("Long SL/TP", "GT Long", 
                     stop=close * (1 - sl_percent/100), 
                     limit=use_tp ? close * (1 + tp_percent/100) : na)

// Short positions (only for hedge)
if short_signal and strategy.position_size >= 0
    strategy.entry("GT Short", strategy.short, qty=position_size)
    
    if use_sl
        strategy.exit("Short SL/TP", "GT Short", 
                     stop=close * (1 + sl_percent/100), 
                     limit=use_tp ? close * (1 - tp_percent/100) : na)

// ===========================
// VISUALIZATION
// ===========================

// Nash Equilibrium Bands
plot(price_mean, "Nash Equilibrium", color=color.orange, linewidth=2)
plot(upper_nash, "Upper Nash", color=color.red, linewidth=1, style=plot.style_line)
plot(lower_nash, "Lower Nash", color=color.green, linewidth=1, style=plot.style_line)

// Background colors
bgcolor(herd_buying ? color.new(color.red, 90) : na, title="Herd Buying")
bgcolor(herd_selling ? color.new(color.green, 90) : na, title="Herd Selling")
bgcolor(institutional_volume ? color.new(color.blue, 95) : na, title="Institutional Volume")

// Signal markers
plotshape(contrarian_buy, "Contrarian Buy", shape.triangleup, location.belowbar, color.green, size=size.small)
plotshape(contrarian_sell, "Contrarian Sell", shape.triangledown, location.abovebar, color.red, size=size.small)

plotshape(liquidity_trap_up, "Liquidity Trap Up", shape.xcross, location.abovebar, color.red, size=size.tiny)
plotshape(liquidity_trap_down, "Liquidity Trap Down", shape.xcross, location.belowbar, color.green, size=size.tiny)