Diamond Strategy: In-Depth Analysis

1. Strategy Overview and Design Philosophy

1.1 Strategy Background

Diamond is a distinctive quantitative trading strategy named in tribute to the resilience of Afghan women — like diamonds buried in desert depths, shining even in darkness. This poetic design philosophy is reflected in the strategy's purity.

1.2 Core Design Philosophy: Minimalism

Unlike most strategies relying on complex technical indicators, Diamond takes a radically different path: completely abandoning pre-calculated technical indicators, using only raw OHLCV data for trading decisions.

This design choice reflects deep insight:

1. Data Purity: Raw market data is the most authentic price reflection; technical indicators are inherently secondary processing that may introduce distortion or lag
2. Computational Efficiency: Omitting indicator calculations makes strategy execution faster, enabling quicker response in fast-moving markets
3. Extensibility: Although the default does not use any indicators, the architecture allows users to freely add custom indicators incorporated into the hyperparameter optimization framework

1.3 Strategy Positioning

Diamond is positioned for:

• Traders who want to mine trading signals from basic data
• Quantitative researchers seeking market patterns through large-scale hyperparameter optimization
• Strategy development frameworks for building more complex systems

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2. Architecture Analysis

2.1 Core Architecture

┌─────────────────────────────────────────────────────────┐
│                    Diamond Strategy                      │
├─────────────────────────────────────────────────────────┤
│  Parameter Definition Layer                             │
│  ├── buy_fast_key, buy_slow_key                        │
│  ├── buy_horizontal_push, buy_vertical_push            │
│  └── sell counterparts                                 │
├─────────────────────────────────────────────────────────┤
│  Entry Logic Layer (populate_entry_trend)               │
│  └── Buy signal based on crossover                      │
├─────────────────────────────────────────────────────────┤
│  Exit Logic Layer (populate_exit_trend)                 │
│  └── Sell signal based on crossover                     │
└─────────────────────────────────────────────────────────┘

2.2 Timeframe

Default 5-minute timeframe (medium-frequency trading), capturing intraday opportunities without excessive transaction costs.

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3. Parameter System

3.1 Buy Parameters

Parameter	Optimization Value	Description
buy_fast_key	high	Use highest price as fast line
buy_slow_key	volume	Use volume as slow line
buy_horizontal_push	7	Time lag of 7 periods
buy_vertical_push	0.942	Volume scaled to 94.2%

Practical interpretation: When highest price (lagged 7 periods) crosses above volume × 0.942, trigger buy signal.

3.2 Sell Parameters

Parameter	Optimization Value	Description
sell_fast_key	high	Use highest price as fast line
sell_slow_key	low	Use lowest price as slow line
sell_horizontal_push	10	Time lag of 10 periods
sell_vertical_push	1.184	Low scaled to 118.4%

Practical interpretation: When highest price (lagged 10 periods) crosses below lowest price × 1.184, trigger sell signal.

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4. Entry Logic

def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    conditions = []
    conditions.append(
        qtpylib.crossed_above(
            dataframe[self.buy_fast_key.value].shift(self.buy_horizontal_push.value),
            dataframe[self.buy_slow_key.value] * self.buy_vertical_push.value
        )
    )
    if conditions:
        dataframe.loc[
            reduce(lambda x, y: x & y, conditions),
            'buy'] = 1
    return dataframe

The essence: comparing current highest price with 7-period-lagged highest price against a volume-derived threshold. This cross-dimensional combination may discover patterns human intuition cannot detect.

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5. Risk Management

5.1 Fixed Stoploss

stoploss = -0.271  # 27.1%

Relatively wide stoploss suits the 5-minute timeframe trend-following style, giving trends sufficient development space.

5.2 ROI Table

minimal_roi = {
    "0": 0.242,    # Immediate: 24.2%
    "13": 0.044,   # After 13 periods: 4.4%
    "51": 0.02,    # After 51 periods: 2%
    "170": 0       # After 170 periods: any profit
}

Time-decreasing design: initially high expectations (24.2%), progressively lowering as holding time extends.

5.3 Trailing Stop

trailing_stop = True
trailing_stop_positive = 0.011  # 1.1%
trailing_stop_positive_offset = 0.054  # 5.4%
trailing_only_offset_is_reached = False

Activates after 5.4% profit, tracking 1.1% below peak price.

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6. Strategy Pros & Cons

Advantages

1. Minimalist design: Clean code, easy to understand and maintain
2. Highly flexible: All core parameters optimizable
3. Computationally efficient: No complex indicator calculations
4. Strong extensibility: Easy to add custom indicators
5. Complete risk management: Stoploss + ROI + trailing stop

Limitations

1. Over-reliance on optimization: Strategy effectiveness highly dependent on hyperparameter quality
2. Lack of traditional technical analysis: No conventional indicators may miss certain signals
3. Poor parameter interpretability: Cross-dimensional combinations' economic meaning not intuitive
4. Out-of-sample risk: Optimized parameters may not adapt to future markets