Constructing and Validating a Quantitative Buying and selling Technique with OctoBot, Stroll-Ahead Backtesting, Parameter Optimization, and Interactive Evaluation

WORKER = os.path.be part of(WORK_DIR, "octobot_worker.py")
WORKER_SRC = r'''
import asyncio, itertools, json, os, sys, time, traceback
import numpy as np
import tulipy
import octobot_script as obs
CFG  = json.load(open(os.environ["OBS_CONFIG"]))
OUT  = os.environ["OBS_OUT"]
FIX  = CFG["fixed"]
for kw in ("Shut", "Excessive", "Low", "Time", "market", "current_live_time", "plot_indicator"):
   if not hasattr(obs, kw):
       increase RuntimeError(
           f"octobot_script.{kw} lacking -> tentacles aren't put in. "
           "Run: python -m octobot_script.cli install_tentacles"
       )
def tail(*arrays):
   """tulipy indicators return totally different lengths; right-align all of them."""
   n = min(len(a) for a in arrays)
   return [np.asarray(a)[-n:] for a in arrays]
def clamp(v):
   return float(min(max(v, FIX["min_offset_pct"]), FIX["max_offset_pct"]))
def build_callbacks(params, run_data):
   """
   OctoBot-Script splits a technique into:
     initialize(ctx) -> runs as soon as on the primary candle. Do vectorised work right here.
     technique(ctx)   -> runs on EVERY closed candle. Maintain it low-cost.
   """
   async def initialize(ctx):
       closes = await obs.Shut(ctx, max_history=True)
       highs  = await obs.Excessive(ctx,  max_history=True)
       lows   = await obs.Low(ctx,   max_history=True)
       occasions  = await obs.Time(ctx,  max_history=True, use_close_time=True)
       rsi  = tulipy.rsi(closes, interval=params["rsi_period"])
       ema_f = tulipy.ema(closes, interval=FIX["ema_fast"])
       ema_s = tulipy.ema(closes, interval=FIX["ema_slow"])
       atr   = tulipy.atr(highs, lows, closes, interval=FIX["atr_period"])
       t, c, rsi, ema_f, ema_s, atr = tail(occasions, closes, rsi, ema_f, ema_s, atr)
       atr_pct = np.the place(c > 0, atr / c * 100.0, 0.0)
       entries, offsets = set(), {}
       for i in vary(len(t)):
           oversold = rsi[i] < params["rsi_threshold"]
           uptrend  = ema_f[i] > ema_s[i]
           if oversold and uptrend and atr_pct[i] > 0:
               ts = float(t[i])
               entries.add(ts)
               offsets[ts] = (
                   clamp(FIX["sl_atr_mult"]     * atr_pct[i]),
                   clamp(params["tp_atr_mult"]  * atr_pct[i]),
               )
       run_data["entries"] = entries
       run_data["offsets"] = offsets
       if run_data.get("plot"):
           await obs.plot_indicator(ctx, f"RSI({params['rsi_period']})", t, rsi, entries)
           await obs.plot_indicator(ctx, f"EMA{FIX['ema_fast']}",  t, ema_f)
           await obs.plot_indicator(ctx, f"EMA{FIX['ema_slow']}",  t, ema_s)
           await obs.plot_indicator(ctx, "ATR %", t, atr_pct)
   async def technique(ctx):
       now = obs.current_live_time(ctx)
       if not in run_data["entries"]:
           return
       sl, tp = run_data["offsets"]1786476726
       await obs.market(
           ctx, "purchase",
           quantity=FIX["position_size"],
           stop_loss_offset=f"-{sl:.2f}%",
           take_profit_offset=f"{tp:.2f}%",
       )
   return initialize, technique
def metrics(res):
   br = res.report.get("bot_report", {})
   first = lambda d: float(listing(d.values())[0]) if isinstance(d, dict) and d else float("nan")
   return {
       "profitability":  first(br.get("profitability", {})),
       "market":         first(br.get("market_average_profitability", {})),
       "reference":      br.get("reference_market"),
       "start_portfolio": str(br.get("starting_portfolio")),
       "end_portfolio":   str(br.get("end_portfolio")),
       "candles":        res.candles_count,
       "duration_s":     spherical(res.length or 0, 2),
       "errors":         res.report.get("errors_count"),
   }
async def load_data(window):
   """Strive every trade till one serves information (Binance blocks many datacenter IPs)."""
   begin, finish = window
   final = None
   for ex in CFG["exchanges"]:
       strive:
           print(f"  ↓ fetching {CFG['symbol']} {CFG['time_frame']} from {ex} "
                 f"[{time.strftime('%Y-%m-%d', time.gmtime(start))} → "
                 f"{time.strftime('%Y-%m-%d', time.gmtime(end))}]", flush=True)
           information = await obs.get_data(
               CFG["symbol"], CFG["time_frame"],
               trade=ex, exchange_type="spot",
               start_timestamp=begin, end_timestamp=finish,
               social_services=[],
           )
           print(f"    ✓ {ex} okay -> {information.data_files}", flush=True)
           return information, ex
       besides Exception as e:
           final = e
           print(f"    ✗ {ex}: {sort(e).__name__}: {e}", flush=True)
   increase RuntimeError(f"no trade served information; final error: {final}")
async def backtest(information, params, plot=False, storage=False):
   run_data = {"entries": None, "offsets": {}, "plot": plot}
   init_f, strat_f = build_callbacks(params, run_data)
   res = await obs.run(
       information, params,
       strategy_func=strat_f,
       initialize_func=init_f,
       enable_logs=False,
       enable_storage=storage,
   )
   return res, len(run_data["entries"] or ())
async def important():
   out = {"grid": [], "finest": None, "oos": None, "errors": []}
   print("n" + "=" * 78 + "n  IN-SAMPLE GRID SEARCHn" + "=" * 78, flush=True)
   is_data, ex_used = await load_data(CFG["in_sample"])
   out["exchange"] = ex_used
   keys  = listing(CFG["grid"].keys())
   combos = [dict(zip(keys, v)) for v in itertools.product(*CFG["grid"].values())]
   print(f"  {len(combos)} configurations to evaluaten", flush=True)
   for i, params in enumerate(combos, 1):
       strive:
           res, n_sig = await backtest(is_data, params)
           m = metrics(res)
           m.replace(params); m["signals"] = n_sig
           m["edge"] = m["profitability"] - m["market"]
           out["grid"].append(m)
           print(f"  [{i:>2}/{len(combos)}] {params}  "
                 f"P&L {m['profitability']:+.2f}%  vs market {m['market']:+.2f}%  "
                 f"edge {m['edge']:+.2f}%  ({n_sig} indicators, {m['duration_s']}s)", flush=True)
       besides Exception as e:
           out["errors"].append(f"{params}: {e}")
           print(f"  [{i:>2}/{len(combos)}] {params} FAILED: {e}", flush=True)
           traceback.print_exc()
   await is_data.cease()
   if not out["grid"]:
       json.dump(out, open(OUT, "w")); increase SystemExit("no profitable runs")
   finest = max(out["grid"], key=lambda r: r["edge"])
   out["best"] = {okay: finest[k] for okay in keys}
   print(f"n  🏆 finest in-sample config: {out['best']}  (edge {finest['edge']:+.2f}%)", flush=True)
   print("n" + "=" * 78 + "n  OUT-OF-SAMPLE VALIDATION (by no means optimised on)n" + "=" * 78,
         flush=True)
   oos_data, _ = await load_data(CFG["out_of_sample"])
   res, n_sig = await backtest(oos_data, out["best"], plot=True, storage=True)
   m = metrics(res); m.replace(out["best"])
   m["signals"] = n_sig; m["edge"] = m["profitability"] - m["market"]
   out["oos"] = m
   print(f"  OOS P&L {m['profitability']:+.2f}%  vs market {m['market']:+.2f}%  "
         f"edge {m['edge']:+.2f}%  ({n_sig} indicators)", flush=True)
   print("  " + res.describe(), flush=True)
   report_dir = os.path.be part of(os.getcwd(), "report")
   os.makedirs(report_dir, exist_ok=True)
   strive:
       plot = await res.plot(report_file=os.path.be part of(report_dir, "report.html"), present=False)
       out["bundle"] = os.path.be part of(os.path.dirname(os.path.abspath(plot.report_file)),
                                    "report.json")
       print(f"  ✓ report bundle: {out['bundle']}", flush=True)
   besides Exception as e:
       out["errors"].append(f"report: {e}")
       print(f"  ✗ report era failed: {e}", flush=True)
   await oos_data.cease()
   json.dump(out, open(OUT, "w"), indent=2, default=str)
   print("n✓ outcomes written to", OUT, flush=True)
asyncio.run(important())
'''
with open(WORKER, "w") as f:
   f.write(WORKER_SRC)