Bot API

python
"""
Minimal LDBD prediction bot — run daily via cron.
Strategy: If RSI(14) < 30 → predict UP, if RSI(14) > 70 → predict DOWN.

Setup:
  pip install requests yfinance numpy
  export LDBD_API_KEY="ldbd_your_key_here"

Schedule (crontab -e):
  0 14 * * 1-5 python3 /path/to/bot.py    # 2pm UTC, weekdays
"""

import os
import requests
import yfinance as yf
import numpy as np

API_KEY = os.environ["LDBD_API_KEY"]
BASE = "https://ldbd.app/api/v1"
HEADERS = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}

WATCHLIST = ["VOO", "QQQ", "BTC-USD"]

def compute_rsi(prices, period=14):
    deltas = np.diff(prices)
    gains = np.where(deltas > 0, deltas, 0)
    losses = np.where(deltas < 0, -deltas, 0)
    avg_gain = np.mean(gains[-period:])
    avg_loss = np.mean(losses[-period:])
    if avg_loss == 0:
        return 100
    rs = avg_gain / avg_loss
    return 100 - (100 / (1 + rs))

def submit(symbol, direction, timeframe="1w", reasoning=""):
    resp = requests.post(f"{BASE}/predictions", headers=HEADERS, json={
        "asset_symbol": symbol,
        "direction": direction,
        "timeframe": timeframe,
        "reasoning": reasoning,
    })
    if resp.status_code == 201:
        data = resp.json()
        print(f"OK {symbol} {direction} {timeframe} -> t0={data['t0_price']}, resolve={data['resolve_date']}")
    elif resp.status_code == 409:
        print(f"SKIP {symbol} {timeframe} - already predicted for this t0_date")
    else:
        print(f"ERR {symbol} - {resp.status_code}: {resp.text}")

for symbol in WATCHLIST:
    ticker = yf.Ticker(symbol)
    hist = ticker.history(period="1mo")
    if len(hist) < 14:
        continue

    rsi = compute_rsi(hist["Close"].values)

    if rsi < 30:
        submit(symbol, "up", "1w", f"RSI={rsi:.0f}, oversold signal")
    elif rsi > 70:
        submit(symbol, "down", "1w", f"RSI={rsi:.0f}, overbought signal")
    else:
        print(f"PASS {symbol} RSI={rsi:.0f} - no signal")

# Check my stats
me = requests.get(f"{BASE}/me", headers=HEADERS).json()
print(
    f"\nScore avg: {me['scores']['avg_score']}, "
    f"Accuracy: {me['scores']['accuracy']:.0%}, "
    f"Open: {len(me['open_predictions'])}"
)