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October 16, 2025 00:44
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Return backtesting for any trading pair on Binance.
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| # grid_backtest_step_both_unique_range_progress.py | |
| # 在上一版基础上新增： | |
| # 每天结束后打印执行进度： | |
| # [arith_step10] finished 2025-10-14 cash=xxxxx pairs=xxx realized=xxx | |
| # 并在日志文件 runs/.../progress.log 中同步写入。 | |
| import csv, os, io, zipfile, urllib.request | |
| from datetime import datetime, timezone, timedelta | |
| from torch.utils.tensorboard import SummaryWriter | |
| # ====== 基本配置 ====== | |
| SYMBOL = "ETHFDUSD" | |
| INTERVAL = "1s" | |
| DATA_DIR = "data" | |
| INVEST_Q = 10_000.0 | |
| FEE_RATE = 0.000 | |
| GRID_LOWER = 3700.0 | |
| GRID_UPPER = 4800.0 | |
| ARITH_STEPS = [5,10,20,30,40,50,60,70,80] | |
| START_DATE = "2025-09-01" | |
| END_DATE = "2025-10-14" | |
| LOGROOT = "runs/grid_step_both_unique_range_progress" | |
| # ====================== | |
| OPEN_TIME=0; OPEN=1; HIGH=2; LOW=3; CLOSE=4; CLOSE_TIME=6 | |
| HOUR_MS = 3_600_000 | |
| def ms13(x:str)->int: return int(str(x).strip()[:13]) | |
| def iso_utc(ms:int)->str: return datetime.fromtimestamp(ms/1000, tz=timezone.utc).strftime("%Y-%m-%d %H:%M:%S") | |
| def daterange_utc(start_date:str, end_date:str): | |
| s=datetime.strptime(start_date,"%Y-%m-%d"); e=datetime.strptime(end_date,"%Y-%m-%d") | |
| while s<=e: | |
| yield s.strftime("%Y-%m-%d") | |
| s+=timedelta(days=1) | |
| def local_csv_path(date_str): return os.path.join(DATA_DIR, f"{SYMBOL}-{INTERVAL}-{date_str}.csv") | |
| def remote_zip_url(date_str): return f"https://data.binance.vision/data/spot/daily/klines/{SYMBOL}/{INTERVAL}/{SYMBOL}-{INTERVAL}-{date_str}.zip" | |
| def ensure_day_csv(date_str): | |
| os.makedirs(DATA_DIR,exist_ok=True) | |
| path=local_csv_path(date_str) | |
| if os.path.exists(path): return path | |
| url=remote_zip_url(date_str) | |
| print(f"[download] {url}") | |
| with urllib.request.urlopen(url) as resp: data=resp.read() | |
| with zipfile.ZipFile(io.BytesIO(data)) as zf: | |
| members=[n for n in zf.namelist() if n.endswith(".csv")] | |
| if not members: raise RuntimeError("no csv in zip") | |
| with zf.open(members[0]) as zcsv, open(path,"wb") as fout: fout.write(zcsv.read()) | |
| print(f"[saved] {path}") | |
| return path | |
| def build_levels_arith(lower,upper,step): | |
| n=int((upper-lower)/step) | |
| if n<1: raise ValueError("N<1") | |
| return [lower+i*step for i in range(n+1)],n | |
| def build_levels_geom_with_N(lower,upper,n): | |
| r=(upper/lower)**(1.0/n) | |
| return [lower*(r**i) for i in range(n+1)] | |
| def price_to_interval_idx(p,levels): | |
| if p<levels[0]: return -1 | |
| if p>=levels[-1]: return len(levels)-1 | |
| lo,hi=0,len(levels)-2 | |
| while lo<=hi: | |
| m=(lo+hi)//2 | |
| if levels[m]<=p<levels[m+1]: return m | |
| if p>=levels[m+1]: lo=m+1 | |
| else: hi=m-1 | |
| return -1 | |
| def try_buy_level(i,levels,fee,per_notional,cash_q,pos_b, | |
| holding,qty,cost,buy_time,buy_price,event_ms): | |
| if holding[i]: return False,cash_q,pos_b | |
| p=levels[i]; denom=p*(1+fee) | |
| if denom<=0: return False,cash_q,pos_b | |
| q=per_notional/denom; pay=per_notional | |
| if pay<=cash_q and q>0: | |
| cash_q-=pay; pos_b+=q | |
| holding[i]=True; qty[i]=q; cost[i]=pay | |
| buy_time[i]=event_ms; buy_price[i]=p | |
| return True,cash_q,pos_b | |
| return False,cash_q,pos_b | |
| def try_sell_level(i,levels,fee,per_notional,cash_q,pos_b, | |
| holding,qty,cost,buy_time,buy_price, | |
| event_ms,pair_writer): | |
| if not holding[i] or qty[i]<=0: return False,0.0,cash_q,pos_b | |
| p_sell=levels[i+1]; q=qty[i] | |
| sell_net=q*p_sell*(1-fee); profit=sell_net-cost[i] | |
| cash_q+=sell_net; pos_b-=q | |
| pair_writer.writerow({ | |
| "level":i,"buy_time":iso_utc(buy_time[i]), | |
| "buy_price":f"{buy_price[i]:.8f}","sell_time":iso_utc(event_ms), | |
| "sell_price":f"{p_sell:.8f}","qty_base":f"{q:.12f}", | |
| "buy_amount_quote":f"{cost[i]:.8f}","sell_amount_quote":f"{sell_net:.8f}", | |
| "profit_quote":f"{profit:.8f}" | |
| }) | |
| holding[i]=False; qty[i]=0; cost[i]=0; buy_time[i]=0; buy_price[i]=0 | |
| return True,profit,cash_q,pos_b | |
| def cross_segment(prev_idx,new_idx,levels, | |
| fee,per_notional, | |
| cash_q,pos_b, | |
| holding,qty,cost,buy_time,buy_price, | |
| event_ms,pair_writer): | |
| realized=0.0; pairs=0; i=prev_idx | |
| if new_idx>prev_idx: | |
| while i<new_idx: | |
| if i==-1: i+=1; continue | |
| ok,prof,cash_q,pos_b=try_sell_level(i,levels,fee,per_notional, | |
| cash_q,pos_b,holding,qty,cost,buy_time,buy_price,event_ms,pair_writer) | |
| if ok: realized+=prof; pairs+=1 | |
| i+=1 | |
| elif new_idx<prev_idx: | |
| while i>new_idx: | |
| if i==len(levels)-1: i-=1; continue | |
| _ok,cash_q,pos_b=try_buy_level(i,levels,fee,per_notional, | |
| cash_q,pos_b,holding,qty,cost,buy_time,buy_price,event_ms) | |
| i-=1 | |
| return new_idx,cash_q,pos_b,realized,pairs | |
| def write_hour(writer,hour_idx,price_c, | |
| inside_cnt,below_cnt,above_cnt, | |
| hour_pairs,total_pairs, | |
| hour_grid_profit,realized_grid_profit, | |
| cash_q,pos_b,invest_q): | |
| denom=max(1,inside_cnt+below_cnt+above_cnt) | |
| equity=cash_q+pos_b*price_c | |
| floating_pnl=equity-invest_q | |
| writer.add_scalar("market/price",price_c,hour_idx) | |
| writer.add_scalar("pairs/hour_pairs",hour_pairs,hour_idx) | |
| writer.add_scalar("pairs/cum_pairs",total_pairs,hour_idx) | |
| writer.add_scalar("grid/grid_profit_hour",hour_grid_profit,hour_idx) | |
| writer.add_scalar("grid/grid_profit_cum",realized_grid_profit,hour_idx) | |
| writer.add_scalar("pnl/floating_pnl",floating_pnl,hour_idx) | |
| writer.add_scalar("pnl/total_pnl",floating_pnl,hour_idx) | |
| writer.flush() | |
| def run_one_config(levels,n_grid,scheme_name,logdir,start_date,end_date): | |
| os.makedirs(logdir,exist_ok=True) | |
| writer=SummaryWriter(logdir,max_queue=1,flush_secs=5) | |
| writer.add_hparams({"grid_lower":GRID_LOWER,"grid_upper":GRID_UPPER, | |
| "grid_num":n_grid,"scheme":scheme_name,"fee":FEE_RATE,"invest":INVEST_Q, | |
| "start":start_date,"end":end_date},{}) | |
| progress_path=os.path.join(logdir,"progress.log") | |
| flog=open(progress_path,"a") | |
| pairs_csv=os.path.join(logdir,"pairs_log.csv") | |
| f=open(pairs_csv,"w",newline="") | |
| pw=csv.DictWriter(f,fieldnames=["level","buy_time","buy_price","sell_time","sell_price", | |
| "qty_base","buy_amount_quote","sell_amount_quote","profit_quote"]) | |
| pw.writeheader() | |
| per_notional=INVEST_Q/n_grid; cash_q=INVEST_Q; pos_b=0.0 | |
| m=len(levels)-1 | |
| holding=[False]*m; qty=[0.0]*m; cost=[0.0]*m | |
| buy_time=[0]*m; buy_price=[0.0]*m | |
| realized_grid_profit=0.0; total_pairs=0 | |
| hour_pairs=0; hour_grid_profit=0.0; hour_idx=0 | |
| inside_cnt=below_cnt=above_cnt=0 | |
| cur_hour_bucket=None; last_price=None | |
| seeded=False; prev_ref=None; prev_idx=None | |
| for day in daterange_utc(start_date,end_date): | |
| path=ensure_day_csv(day) | |
| with open(path,"r") as fr: | |
| r=csv.reader(fr) | |
| for row in r: | |
| ot_ms=ms13(row[OPEN_TIME]); ct_ms=ms13(row[CLOSE_TIME]) | |
| o=float(row[OPEN]); h=float(row[HIGH]); l=float(row[LOW]); c=float(row[CLOSE]) | |
| if not seeded: | |
| p0=c; idx0=price_to_interval_idx(p0,levels) | |
| if idx0>=0: | |
| for i in range(0,idx0+1): | |
| ok,cash_q,pos_b=try_buy_level(i,levels,FEE_RATE,per_notional, | |
| cash_q,pos_b,holding,qty,cost,buy_time,buy_price,ot_ms) | |
| if not ok: break | |
| seeded=True | |
| hb=ot_ms//HOUR_MS | |
| if cur_hour_bucket is None: cur_hour_bucket=hb; hour_idx=1 | |
| elif hb!=cur_hour_bucket: | |
| write_hour(writer,hour_idx,last_price or c, | |
| inside_cnt,below_cnt,above_cnt, | |
| hour_pairs,total_pairs,hour_grid_profit,realized_grid_profit, | |
| cash_q,pos_b,INVEST_Q) | |
| hour_idx+=1; cur_hour_bucket=hb | |
| hour_pairs=hour_grid_profit=0.0 | |
| inside_cnt=below_cnt=above_cnt=0 | |
| last_price=c | |
| if c<=levels[0]: below_cnt+=1 | |
| elif c>=levels[-1]: above_cnt+=1 | |
| else: inside_cnt+=1 | |
| if prev_ref is None: | |
| prev_ref=o; prev_idx=price_to_interval_idx(o,levels) | |
| path_seq=([h,l,c] if c>=o else [l,h,c]) | |
| cur=o; cur_idx=price_to_interval_idx(cur,levels) | |
| for p in path_seq: | |
| new_idx=price_to_interval_idx(p,levels) | |
| cur_idx,cash_q,pos_b,realized,pairs=cross_segment(cur_idx,new_idx,levels, | |
| FEE_RATE,per_notional,cash_q,pos_b, | |
| holding,qty,cost,buy_time,buy_price,ot_ms,pw) | |
| realized_grid_profit+=realized | |
| hour_grid_profit+=realized | |
| total_pairs+=pairs | |
| hour_pairs+=pairs | |
| cur=p | |
| prev_ref=c; prev_idx=cur_idx | |
| # 每天结束打印进度 | |
| equity=cash_q+pos_b*(last_price or c) | |
| line=f"[{scheme_name}] finished {day} cash={cash_q:.2f} equity={equity:.2f} pairs={total_pairs} realized={realized_grid_profit:.2f}" | |
| print(line); flog.write(line+"\n"); flog.flush() | |
| # 收尾 | |
| if cur_hour_bucket is not None and last_price is not None: | |
| write_hour(writer,hour_idx,last_price,inside_cnt,below_cnt,above_cnt, | |
| hour_pairs,total_pairs,hour_grid_profit,realized_grid_profit, | |
| cash_q,pos_b,INVEST_Q) | |
| f.close(); flog.close(); writer.close() | |
| def main(): | |
| abs_root=os.path.abspath(LOGROOT) | |
| for step in ARITH_STEPS: | |
| levels_arith,n=build_levels_arith(GRID_LOWER,GRID_UPPER,step) | |
| run_one_config(levels_arith,n,f"arith_step{step}",f"{LOGROOT}/arith_step{step}",START_DATE,END_DATE) | |
| levels_geom=build_levels_geom_with_N(GRID_LOWER,GRID_UPPER,n) | |
| run_one_config(levels_geom,n,f"geom_step{step}",f"{LOGROOT}/geom_N{n}_from_step{step}",START_DATE,END_DATE) | |
| print("TensorBoard logdir:",os.path.abspath(LOGROOT)) | |
| if __name__=="__main__": | |
| main() | |
| ''' | |
| 以下是针对你当前网格交易回测工程的 **TensorBoard 数据查看与目录命名说明文档**。 | |
| --- | |
| ## 📘 使用说明文档 | |
| ### 一、TensorBoard 数据查看方式 | |
| #### 1. 运行代码并启动 TensorBoard | |
| 在项目根目录下执行： | |
| ```bash | |
| python main.py | |
| tensorboard --logdir runs --port 6006 | |
| ``` | |
| 然后打开浏览器访问： | |
| ``` | |
| http://localhost:6006 | |
| ``` | |
| #### 2. 数据目录结构 | |
| 运行脚本后，会自动生成： | |
| ``` | |
| runs/ | |
| ├── grid_step_both_unique_range_progress/ | |
| │ ├── arith_step5/ | |
| │ ├── arith_step10/ | |
| │ ├── arith_step20/ | |
| │ ├── ... | |
| │ ├── geom_step5/ | |
| │ ├── geom_step10/ | |
| │ ├── ... | |
| ``` | |
| 每个子文件夹对应一个**独立的策略配置**（一种网格划分方案），其内部包含： | |
| ``` | |
| events.out.tfevents... # TensorBoard 原始日志 | |
| pairs_log.csv # 每次网格配对完成的详细成交记录 | |
| progress.log # 每天计算完成后的进度信息（方便断点监控） | |
| ``` | |
| --- | |
| ### 二、目录命名规则 | |
| | 文件夹名 | 含义 | 示例 | | |
| | --------------------- | -------------------------------------- | ----------------------------------- | | |
| | `arith_step5` | 等差网格，步长为 5 美元 | 下限 3500，上限 4500，每 5 美元一个格，共 200 个网格 | | |
| | `geom_step5` | 等比网格，网格数量与等差方案相同（如上例为 19 个区间），指数比率自动计算 | 用于比较等差与等比划分的收益差异 | | |
| 每个配置的资金分配规则一致： | |
| ``` | |
| 每格资金 = 总投资金额 / 网格数量 | |
| ``` | |
| --- | |
| ### 三、TensorBoard 面板说明 | |
| | 指标路径 | 含义 | 单位/说明 | | |
| | ---------------------- | ------------------------------- | ----- | | |
| | `market/price` | 每小时的 ETH 现价（取该小时最后一秒的 Close） | 美元 | | |
| | `pairs/hour_pairs` | 该小时完成的配对次数（一次完整买入+卖出） | 次 | | |
| | `pairs/cum_pairs` | 累计配对次数（从首日首秒起） | 次 | | |
| | `grid/grid_profit_hour` | 该小时内已实现的网格收益 | USD | | |
| | `grid/grid_profit_cum` | 从开始到当前累计的已实现网格收益 | USD | | |
| | `pnl/floating_pnl` | 含持仓浮盈亏（当前资产市值-初始投资） | USD | | |
| | `pnl/total_pnl` | 总体盈亏（与 floating_pnl 相同，这里即净值变化） | USD | | |
| --- | |
| ### 四、辅助文件说明 | |
| | 文件 | 说明 | | |
| | ------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------- | | |
| | `pairs_log.csv` | 每笔配对的成交记录，包括买入价/时间、卖出价/时间、数量、收益等。<br>格式：<br>`level,buy_time,buy_price,sell_time,sell_price,qty_base,buy_amount_quote,sell_amount_quote,profit_quote` | | |
| | `progress.log` | 每天结束后写入一行运行状态：<br>`[arith_step10] finished 2025-10-15 cash=9876.12 equity=10123.45 pairs=182 realized=123.45` | | |
| | `events.out.tfevents...` | TensorBoard 用于绘图的原始日志文件，可直接用 TensorBoard 打开。 | | |
| --- | |
| ### 五、查看多个配置对比 | |
| 在 TensorBoard 左侧勾选多个文件夹（如 `arith_step10` 与 `geom_step10`），即可在同一面板中对比： | |
| * 不同网格数量或步长下的收益曲线； | |
| * 等差与等比配置的配对效率； | |
| * 不同网格规模下的浮动净值变化趋势。 | |
| 常见对比指标： | |
| * `pairs/cum_pairs` → 网格活跃度； | |
| * `grid/grid_profit_cum` → 已实现的网格收益； | |
| * `pnl/floating_pnl` → 含未平仓盈亏的总表现。 | |
| --- | |
| ### 六、调试与恢复 | |
| * 若回测中断，可通过 `progress.log` 查看已完成的最后日期； | |
| * 已下载的 CSV 文件保存在 `data/` 文件夹下； | |
| * 程序会自动跳过已存在的文件，不会重复下载。 | |
| --- | |
| ### 七、建议的分析流程 | |
| 1. 确认 `START_DATE` 与 `END_DATE`，执行脚本； | |
| 2. 打开 TensorBoard，选择 `grid/grid_profit_cum` 与 `pnl/total_pnl`； | |
| 3. 观察等差、等比不同步长下的累计收益曲线； | |
| 4. 打开 `pairs_log.csv` 查看成交分布与区间活跃度； | |
| 5. 结合 `progress.log` 监控每日资金流与运行完成进度。 | |
| ''' |
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