Screener

下載美股和加密貨幣的歷史數據並透過自己的策略去找到強勢標的

The purpose of this project is to download historical data for US stocks and cryptocurrencies, and use different strategies to identify strong performing assets.

Installation

pip3 install -r requirements.txt

API keys are needed for Polygon and Stocksymbol (Not a requirement for Crypto usage)

Rename api_keys.json.example to api_keys.json

Usage

1. Crypto Screener

Identify strong performing cryptocurrencies by comparing them with SMA-30, SMA-45 and SMA-60.

python3 crypto_screener.py
python3 crypto_screener.py -t "15m" -d 3

Options:

• -t Time frame (5m, 15m, 30m, 1h, 2h, 4h, 8h, 1d) (default: 15m)
• -d Calculation duration in days (default: 3 days)

The script will generate a TXT file in output/<date>/ directory that can be imported into TradingView's watchlist.

2. Stock Screener

Analyze US stocks using relative strength calculation and Minervini trend template conditions.

python3 stock_screener.py
python3 stock_screener.py -a -g 

Options:

• -a, --all Include all strong targets in output instead of just top 980 (TradingView has import limit)
• -g Ignore Minervini trend template conditions and calculate RS score only

The script will generate a TXT file in output/<date>/ directory that can be imported into TradingView's watchlist.

3. Crypto Trend Screener

Find cryptocurrencies with price patterns similar to predefined reference trends using Dynamic Time Warping (DTW) algorithms.

python3 crypto_trend_screener.py
python3 crypto_trend_screener.py --asset=crypto -f strong_targets.txt -k 10 -s 1.0

Options:

• -f, --file Path to strong target file (default: use all available symbols)
• --asset Asset type: 'crypto' or 'stock' (default: crypto)
• -nv, --no_visualize Disable DTW alignment visualizations
• -k, --topk Number of top symbols to record per reference trend (default: 6)
• -s, --sleep Sleep time between API requests in seconds (default: 0.5)

Key Parameters:

• REFERENCE_TRENDS: Define reference patterns in format [start_datetime, end_datetime, timeframe, label]
• TIMEFRAMES_TO_ANALYZE: Analysis timeframes, default ["15m", "30m", "1h", "2h", "4h"]
• DTW_WINDOW_RATIO: Controls horizontal matching flexibility (default: 0.2)
• PRICE_WEIGHT/DIFF_WEIGHT: Balance price vs moving average matching (default: 0.4/0.6)

The script generates TXT files and visualization charts in similarity_output/<timestamp>/ directory.

4. Crypto Historical Trend Finder

Search historical cryptocurrency data for patterns similar to reference trends and analyze their future price movements with statistical insights.

python3 crypto_historical_trend_finder.py
python3 crypto_historical_trend_finder.py -k 500 -s 10

Options:

• -k, --topk Number of top matches to keep per reference trend (default: 300)
• -s, --sleep Sleep time between API requests in seconds (default: 15)

Key Features:

• Multi-timeframe pattern matching (15m, 30m, 1h, 2h, 4h)
• Future trend prediction with rise/fall statistics
• Three-panel visualizations (past + pattern + future)
• Statistical analysis with multiple extension factors (0.25x to 2.5x)
• Overlap filtering for clean analysis samples

Key Parameters:

• REFERENCE_TRENDS: Define reference patterns in format [start_datetime, end_datetime, timeframe, label]. Choose patterns with similar high/low bar counts and end at ideal entry points
• TIMEZONE: Timezone setting for datetime display and reference trend parsing (default: "America/Los_Angeles")
• HISTORICAL_START_DATE: Starting date for historical data collection (default: 2021-01-01)
• TIMEFRAMES_TO_ANALYZE: Analysis timeframes for pattern searching (default: ["15m", "30m", "1h", "2h", "4h"])
• VIS_EXTENSION_FUTURE_LENGTH_FACTOR: Determines how far into the future to observe after pattern completion for rise/fall classification. For example, 2.0 means observing 2x the pattern length into the future (default: 2.0)
• EXTENSION_FACTORS_FOR_STATS: Multiple factors for comprehensive statistical analysis [0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5]
• GLOBAL_OVERLAP_FILTERING:
  • True: No overlapping patterns across all symbols (stricter, higher quality)
  • False: Allow overlaps between different symbols (more samples)

Statistical Output Example:

Extension Factor Analysis:
  0.25x: Rise 198(43.5%) | Fall 254(55.8%) | Insufficient 3(0.7%)
  0.5x: Rise 191(42.0%) | Fall 259(56.9%) | Insufficient 5(1.1%)
  0.75x: Rise 186(40.9%) | Fall 260(57.1%) | Insufficient 9(2.0%)
  1.0x: Rise 169(37.1%) | Fall 275(60.4%) | Insufficient 11(2.4%)
  1.5x: Rise 166(36.5%) | Fall 268(58.9%) | Insufficient 21(4.6%)
  2.0x: Rise 159(34.9%) | Fall 267(58.7%) | Insufficient 29(6.4%)
  2.5x: Rise 167(36.7%) | Fall 250(54.9%) | Insufficient 38(8.4%)

Results are saved in past_similar_trends_report/<timestamp>/ directory with comprehensive visualizations and statistical reports.

Dynamic Time Warping (DTW)

DTW is an algorithm for measuring similarity between two temporal sequences that may vary in speed. Unlike Euclidean distance, DTW can handle sequences of unequal lengths and is invariant to time shifts, making it ideal for financial pattern matching.

Applications in this project:

• Price pattern similarity detection
• Moving average relationship matching
• Historical trend analysis with future outcome prediction

Relative Strength Formula

The RS score is calculated using a weighted sum of relative strength indicators:

$$ bars = \text{total bars (depend on time frame, e.g. } 4 \times 24 \times days \text{ for 15m})$$

$$ W_i = e^{2 \times \ln(2) \times i / bars} $$

$$ \begin{align*} N_i & = \frac{(P_i - MA30_i) + (P_i - MA45_i) + (P_i - MA60_i) + (MA30_i - MA45_i) + (MA30_i - MA60_i) + (MA45_i - MA60_i)}{ATR_i}\\ \end{align*} $$

$$ Score = \frac{\sum_{i=1}^{bars} N_i \times W_i}{\sum_{i=1}^{bars} W_i} $$

Where:

• Weight is calculated as an exponential function to make the weight at the midpoint (L/2) is exactly half of the weight at the endpoint (L)
• ATR (Average True Range) is used for normalization

Configuration

Reference Trends (for DTW-based tools)

Define reference patterns in the REFERENCE_TRENDS dictionary:

REFERENCE_TRENDS = {
    "AVAX": [
        [datetime(2023, 11, 9, 12, 0), datetime(2023, 11, 14, 15, 0), "1h", "standard"],
    ],
    "SOL": [
        [datetime(2023, 9, 23, 0, 0), datetime(2023, 10, 15, 21, 0), "4h", "standard"]
    ],
}

Tips:

• Choose patterns with similar high/low bar counts for better normalization
• End patterns at ideal entry points
• Consider different timeframes for various market conditions

Output Files

Scripts save results with the following patterns:

• Crypto Screener: output/<YYYY-MM-DD>/<timestamp>_crypto_<timeframe>_strong_targets.txt
• Stock Screener: output/<YYYY-MM-DD>/<timestamp>_stock_strong_targets_<top_980|all>.txt
• Crypto Trend Screener: similarity_output/<timestamp>/<timestamp>_similar_trend_tradingview.txt + visualization PNG files
• Historical Trend Finder: past_similar_trends_report/<timestamp>/overall_summary.txt + detailed analysis + visualization PNG files

Visualization Features:

• Candlestick charts with volume bars
• Moving average overlays (SMA-30, SMA-45, SMA-60)
• DTW alignment connection lines
• Normalized price scales for fair comparison
• Three-panel analysis (past/pattern/future) for historical finder

All TXT output files are compatible with TradingView watchlist import format.

License

MIT