commit 78d972b59bdb3bbf6e5d08554326fa1bf26c37cf Author: ddrwode <34234@3来 34> Date: Mon Feb 16 22:03:12 2026 +0800 哈哈 diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..b6b1ecf --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,10 @@ +# 默认忽略的文件 +/shelf/ +/workspace.xml +# 已忽略包含查询文件的默认文件夹 +/queries/ +# Datasource local storage ignored files +/dataSources/ +/dataSources.local.xml +# 基于编辑器的 HTTP 客户端请求 +/httpRequests/ diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/jyx_code.iml b/.idea/jyx_code.iml new file mode 100644 index 0000000..ed4165f --- /dev/null +++ b/.idea/jyx_code.iml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..07f4a55 --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,7 @@ + + + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..b569b6b --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/main.py b/main.py new file mode 100644 index 0000000..b56c695 --- /dev/null +++ b/main.py @@ -0,0 +1,16 @@ +# 这是一个示例 Python 脚本。 + +# 按 ⌃R 执行或将其替换为您的代码。 +# 按 双击 ⇧ 在所有地方搜索类、文件、工具窗口、操作和设置。 + + +def print_hi(name): + # 在下面的代码行中使用断点来调试脚本。 + print(f'Hi, {name}') # 按 ⌘F8 切换断点。 + + +# 按装订区域中的绿色按钮以运行脚本。 +if __name__ == '__main__': + print_hi('PyCharm') + +# 访问 https://www.jetbrains.com/help/pycharm/ 获取 PyCharm 帮助 diff --git a/models/__init__.py b/models/__init__.py new file mode 100644 index 0000000..ca256ce --- /dev/null +++ b/models/__init__.py @@ -0,0 +1,58 @@ +from pathlib import Path + +from peewee import * + +# 连接到 SQLite 数据库，如果文件不存在会自动创建 +db = SqliteDatabase(fr'{Path(__file__).parent}/database.db') + +import pymysql + +from peewee import * +from playhouse.pool import PooledMySQLDatabase + +pymysql.install_as_MySQLdb() + +# 数据库配置 +db_config = { + 'database': 'lm', + 'user': 'lm', + 'password': 'HhyAsGbrrbsJfpyy', + 'host': '192.168.1.87', + 'port': 3306 +} + +# 全局数据库实例 +db1 = MySQLDatabase( + db_config['database'], + user=db_config['user'], + password=db_config['password'], + host=db_config['host'], + port=db_config['port'] +) + +# class BaseModel(Model): +# class Meta: +# database = db1 +# +# def save(self, *args, **kwargs): +# """在调用 save 时自动连接和关闭（若无事务）""" +# db.connect(reuse_if_open=True) +# try: +# result = super().save(*args, **kwargs) +# finally: +# # 若当前没有事务且连接仍然打开，则关闭连接 +# if not db.in_transaction() and not db.is_closed(): +# db.close() +# return result +# +# @classmethod +# def get_or_create(cls, defaults=None, **kwargs): +# """在调用 get_or_create 时自动连接和关闭（若无事务）""" +# db.connect(reuse_if_open=True) +# try: +# obj, created = super().get_or_create(defaults=defaults, **kwargs) +# finally: +# # 若当前没有事务且连接仍然打开，则关闭连接 +# if not db.in_transaction() and not db.is_closed(): +# db.close() +# return obj, created diff --git a/models/bitmart.py b/models/bitmart.py new file mode 100644 index 0000000..106fb7a --- /dev/null +++ b/models/bitmart.py @@ -0,0 +1,21 @@ +from peewee import * + +from models import db + + +class BitMart30(Model): + id = IntegerField(primary_key=True) # 时间戳（毫秒级） + open = FloatField(null=True) + high = FloatField(null=True) + low = FloatField(null=True) + close = FloatField(null=True) + + class Meta: + database = db + table_name = 'bitmart_30' + + +# 连接到数据库 +db.connect() +# 创建表（如果表不存在） +db.create_tables([BitMart30]) diff --git a/models/bitmart_15.py b/models/bitmart_15.py new file mode 100644 index 0000000..5360111 --- /dev/null +++ b/models/bitmart_15.py @@ -0,0 +1,21 @@ +from peewee import * + +from models import db + + +class BitMart15(Model): + id = IntegerField(primary_key=True) # 时间戳（毫秒级） + open = FloatField(null=True) + high = FloatField(null=True) + low = FloatField(null=True) + close = FloatField(null=True) + + class Meta: + database = db + table_name = 'bitmart_15' + + +# 连接到数据库 +db.connect() +# 创建表（如果表不存在） +db.create_tables([BitMart15]) diff --git a/models/bitmart_klines.py b/models/bitmart_klines.py new file mode 100644 index 0000000..d80f162 --- /dev/null +++ b/models/bitmart_klines.py @@ -0,0 +1,97 @@ +""" +BitMart 多周期K线数据模型 +包含 1分钟、3分钟、5分钟、15分钟、30分钟、1小时 K线数据表 +""" + +from peewee import * +from models import db + + +# ==================== 1分钟 K线 ==================== +class BitMartETH1M(Model): + id = BigIntegerField(primary_key=True) # 时间戳（毫秒级） + open = FloatField(null=True) + high = FloatField(null=True) + low = FloatField(null=True) + close = FloatField(null=True) + + class Meta: + database = db + table_name = 'bitmart_eth_1m' + + +# ==================== 3分钟 K线 ==================== +class BitMartETH3M(Model): + id = BigIntegerField(primary_key=True) + open = FloatField(null=True) + high = FloatField(null=True) + low = FloatField(null=True) + close = FloatField(null=True) + + class Meta: + database = db + table_name = 'bitmart_eth_3m' + + +# ==================== 5分钟 K线 ==================== +class BitMartETH5M(Model): + id = BigIntegerField(primary_key=True) + open = FloatField(null=True) + high = FloatField(null=True) + low = FloatField(null=True) + close = FloatField(null=True) + + class Meta: + database = db + table_name = 'bitmart_eth_5m' + + +# ==================== 15分钟 K线 ==================== +class BitMartETH15M(Model): + id = BigIntegerField(primary_key=True) + open = FloatField(null=True) + high = FloatField(null=True) + low = FloatField(null=True) + close = FloatField(null=True) + + class Meta: + database = db + table_name = 'bitmart_eth_15m' + + +# ==================== 30分钟 K线 ==================== +class BitMartETH30M(Model): + id = BigIntegerField(primary_key=True) + open = FloatField(null=True) + high = FloatField(null=True) + low = FloatField(null=True) + close = FloatField(null=True) + + class Meta: + database = db + table_name = 'bitmart_eth_30m' + + +# ==================== 1小时 K线 ==================== +class BitMartETH1H(Model): + id = BigIntegerField(primary_key=True) + open = FloatField(null=True) + high = FloatField(null=True) + low = FloatField(null=True) + close = FloatField(null=True) + + class Meta: + database = db + table_name = 'bitmart_eth_1h' + + +# 连接数据库并创建表 +db.connect(reuse_if_open=True) +db.create_tables([ + BitMartETH1M, + BitMartETH3M, + BitMartETH5M, + BitMartETH15M, + BitMartETH30M, + BitMartETH1H, +], safe=True) diff --git a/models/database.db b/models/database.db new file mode 100644 index 0000000..9b0a1dd Binary files /dev/null and b/models/database.db differ diff --git a/models/eth_ai_strategy/feature_cols.json b/models/eth_ai_strategy/feature_cols.json new file mode 100644 index 0000000..9b782ed --- /dev/null +++ b/models/eth_ai_strategy/feature_cols.json @@ -0,0 +1,47 @@ +[ + "bb_pct", + "bb_width", + "keltner_pct", + "keltner_width", + "donchian_pct", + "donchian_width", + "stoch_k", + "stoch_d", + "cci", + "willr", + "rsi", + "atr_band_pct", + "atr_band_width", + "zscore", + "zscore_abs", + "lr_pct", + "lr_width", + "price_vs_median", + "median_band_pct", + "pct_rank", + "chandelier_pct", + "chandelier_width", + "std_band_pct", + "std_band_width", + "elder_bull", + "elder_bear", + "elder_dist", + "ema_fast_slow", + "price_vs_ema120", + "ema8_slope", + "macd", + "macd_signal", + "macd_hist", + "atr_pct", + "ret_1", + "ret_3", + "ret_5", + "ret_10", + "ret_20", + "vol_5", + "vol_20", + "body_pct", + "price_position_20", + "hour_sin", + "hour_cos" +] \ No newline at end of file diff --git a/models/eth_ai_strategy/model.txt b/models/eth_ai_strategy/model.txt new file mode 100644 index 0000000..975acd4 --- /dev/null +++ b/models/eth_ai_strategy/model.txt @@ -0,0 +1,14435 @@ +tree +version=v4 +num_class=3 +num_tree_per_iteration=3 +label_index=0 +max_feature_idx=44 +objective=multiclass num_class:3 +feature_names=Column_0 Column_1 Column_2 Column_3 Column_4 Column_5 Column_6 Column_7 Column_8 Column_9 Column_10 Column_11 Column_12 Column_13 Column_14 Column_15 Column_16 Column_17 Column_18 Column_19 Column_20 Column_21 Column_22 Column_23 Column_24 Column_25 Column_26 Column_27 Column_28 Column_29 Column_30 Column_31 Column_32 Column_33 Column_34 Column_35 Column_36 Column_37 Column_38 Column_39 Column_40 Column_41 Column_42 Column_43 Column_44 +feature_infos=[-0.56020860473416845:1.5378211973321756] [0.0012002529405900091:0.52389054802341473] [-2.5079334222202467:3.2849725847998523] [0.0019716498960659192:0.64034197829291439] [0:1] [0.0018229166666667357:1.1726542739974952] [0:100] [0:99.829059829060029] [-282.86421970945179:278.13386758528179] [-100:0] [1.1787819253438414:99.874371859296602] [-2.6793525179856155:3.4851550680786652] [0.0019719130986470081:0.67272935937751044] [-4.2408344189366698:4.1512847893286473] [0:4.2408344189366698] [-0.74246448021080014:1.748608057274643] [0.0012001010795618296:0.65026623812249462] [-4.4604888198039792:4.3301819777791133] [-0.61512220495099579:1.5825454944447783] [0:1] [-35888059530608:61748573015901] [-0.15266756583989555:0.75179315687136794] [-0.36744229136265139:1.3575343509879696] [0.00084795455972372954:0.43787232105041124] [-0.09310602429380109:1.8935594188022684] [-0.2625315563705265:0.060406474640334683] [-0.16035731319044891:0.14321881934722744] [-0.075897810557044129:0.066784342908162278] [-0.41611835195121744:0.099514682137951394] [-0.10336771155825775:0.14480712656100314] [-0.06638115411078882:0.051375664828636869] [-0.058320881863950333:0.039003712465334663] [-0.026579119389489578:0.02845677128463928] [0.00044762757385856665:0.20529267663744197] [-0.099351429257746871:0.13743341404358356] [-0.13098669283129938:0.30040969992248923] [-0.18704103671706263:0.35502480673820225] [-0.21718462178382547:0.30379600784585215] [-0.23957651817046333:0.42898350063459079] [3.6613139366064048e-05:0.07080954596308911] [0.00028294181914548418:0.051222137346148389] [0:0.12091280653950956] [0:1] [-1:1] [-1:1] +tree_sizes=3446 3461 3443 3527 3580 3581 3530 3565 3566 3565 3577 3559 3551 3592 3584 3574 3562 3588 3569 3576 3526 3592 3576 3563 3595 3597 3577 3554 3572 3575 3569 3595 3572 3591 3572 3578 3604 3575 3582 3580 3588 3578 3583 3573 3575 3592 3575 3585 3577 3580 3595 3572 3586 3566 3580 3575 3582 3554 3570 3587 3581 3599 3596 3593 3594 3596 3580 3602 3581 3582 3587 3601 3573 3578 3583 3577 3584 3573 3565 3599 3592 3568 3584 3608 3577 3586 3553 3585 3601 3568 3589 3591 3589 3579 3578 3605 3586 3581 3544 3526 3600 3581 3567 3610 3585 3580 3601 3605 3604 3600 3571 3564 3613 3588 3570 3597 3564 3572 3606 3578 3558 3583 3570 3573 3618 3563 3568 3592 3588 3557 3572 3579 3567 3560 3567 3561 3580 3592 3548 3570 3586 3575 3599 3584 3561 3581 3519 3573 3579 3582 3554 3594 3569 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0.00377527 +internal_weight=34056.1 6204.7 6148.73 514.393 5634.33 5482.21 4264.83 1217.38 55.9707 37.8896 152.12 98.7504 27851.4 3923.49 3285.19 442.254 638.307 23927.9 993.106 951.534 615.823 527.093 2842.93 2820.25 924.817 412.074 88.7306 22934.8 5956.91 1944.19 +internal_count=105367 17514 17353 1466 15887 15462 12053 3409 161 107 425 273 87853 11769 9817 1235 1952 76084 3166 3036 1886 1623 8582 8520 2961 1152 263 72918 18165 6033 +is_linear=0 +shrinkage=0.05 + + +end of trees + +feature_importances: +Column_28=1438 +Column_43=1271 +Column_31=1156 +Column_44=1144 +Column_21=1075 +Column_40=1041 +Column_5=876 +Column_3=735 +Column_20=728 +Column_39=724 +Column_23=653 +Column_33=607 +Column_32=569 +Column_30=527 +Column_1=505 +Column_10=505 +Column_12=497 +Column_38=478 +Column_4=455 +Column_36=436 +Column_7=434 +Column_15=418 +Column_35=396 +Column_37=378 +Column_27=371 +Column_16=369 +Column_29=320 +Column_41=317 +Column_6=308 +Column_2=300 +Column_24=287 +Column_34=277 +Column_17=275 +Column_22=268 +Column_25=267 +Column_26=253 +Column_19=252 +Column_14=229 +Column_11=228 +Column_8=222 +Column_9=158 +Column_18=131 +Column_0=128 +Column_42=107 +Column_13=48 + +parameters: +[boosting: gbdt] +[objective: multiclass] +[metric: multi_logloss] +[tree_learner: serial] +[device_type: cpu] +[data_sample_strategy: bagging] +[data: ] +[valid: ] +[num_iterations: 250] +[learning_rate: 0.05] +[num_leaves: 31] +[num_threads: -1] +[seed: 42] +[deterministic: 0] +[force_col_wise: 0] +[force_row_wise: 0] +[histogram_pool_size: -1] +[max_depth: 6] +[min_data_in_leaf: 50] +[min_sum_hessian_in_leaf: 0.001] +[bagging_fraction: 0.8] +[pos_bagging_fraction: 1] +[neg_bagging_fraction: 1] +[bagging_freq: 0] +[bagging_seed: 400] +[bagging_by_query: 0] +[feature_fraction: 0.8] +[feature_fraction_bynode: 1] +[feature_fraction_seed: 30056] +[extra_trees: 0] +[extra_seed: 12879] +[early_stopping_round: 0] +[early_stopping_min_delta: 0] +[first_metric_only: 0] +[max_delta_step: 0] +[lambda_l1: 0.1] +[lambda_l2: 0.1] +[linear_lambda: 0] +[min_gain_to_split: 0] +[drop_rate: 0.1] +[max_drop: 50] +[skip_drop: 0.5] +[xgboost_dart_mode: 0] +[uniform_drop: 0] +[drop_seed: 17869] +[top_rate: 0.2] +[other_rate: 0.1] +[min_data_per_group: 100] +[max_cat_threshold: 32] +[cat_l2: 10] +[cat_smooth: 10] +[max_cat_to_onehot: 4] +[top_k: 20] +[monotone_constraints: ] +[monotone_constraints_method: basic] +[monotone_penalty: 0] +[feature_contri: ] +[forcedsplits_filename: ] +[refit_decay_rate: 0.9] +[cegb_tradeoff: 1] +[cegb_penalty_split: 0] +[cegb_penalty_feature_lazy: ] +[cegb_penalty_feature_coupled: ] +[path_smooth: 0] +[interaction_constraints: ] +[verbosity: -1] +[saved_feature_importance_type: 0] +[use_quantized_grad: 0] +[num_grad_quant_bins: 4] +[quant_train_renew_leaf: 0] +[stochastic_rounding: 1] +[linear_tree: 0] +[max_bin: 255] +[max_bin_by_feature: ] +[min_data_in_bin: 3] +[bin_construct_sample_cnt: 200000] +[data_random_seed: 175] +[is_enable_sparse: 1] +[enable_bundle: 1] +[use_missing: 1] +[zero_as_missing: 0] +[feature_pre_filter: 1] +[pre_partition: 0] +[two_round: 0] +[header: 0] +[label_column: ] +[weight_column: ] +[group_column: ] +[ignore_column: ] +[categorical_feature: ] +[forcedbins_filename: ] +[precise_float_parser: 0] +[parser_config_file: ] +[objective_seed: 16083] +[num_class: 3] +[is_unbalance: 0] +[scale_pos_weight: 1] +[sigmoid: 1] +[boost_from_average: 1] +[reg_sqrt: 0] +[alpha: 0.9] +[fair_c: 1] +[poisson_max_delta_step: 0.7] +[tweedie_variance_power: 1.5] +[lambdarank_truncation_level: 30] +[lambdarank_norm: 1] +[label_gain: ] +[lambdarank_position_bias_regularization: 0] +[eval_at: ] +[multi_error_top_k: 1] +[auc_mu_weights: ] +[num_machines: 1] +[local_listen_port: 12400] +[time_out: 120] +[machine_list_filename: ] +[machines: ] +[gpu_platform_id: -1] +[gpu_device_id: -1] +[gpu_use_dp: 0] +[num_gpu: 1] + +end of parameters + +pandas_categorical:null diff --git a/models/eth_ai_strategy/strategy_params.json b/models/eth_ai_strategy/strategy_params.json new file mode 100644 index 0000000..dc95168 --- /dev/null +++ b/models/eth_ai_strategy/strategy_params.json @@ -0,0 +1,40 @@ +{ + "bb_period": 20, + "bb_std": 2.0, + "keltner_period": 20, + "keltner_atr_mult": 2.0, + "donchian_period": 20, + "stoch_k_period": 14, + "stoch_d_period": 3, + "cci_period": 20, + "willr_period": 14, + "rsi_period": 14, + "atr_band_period": 20, + "atr_band_mult": 2.0, + "zscore_period": 20, + "lr_period": 20, + "lr_std_mult": 2.0, + "median_band_period": 20, + "pct_rank_period": 20, + "chandelier_period": 22, + "chandelier_mult": 3.0, + "std_band_period": 14, + "std_band_mult": 2.0, + "elder_period": 13, + "forward_bars": 10, + "label_threshold": 0.002, + "prob_threshold": 0.45, + "sl_pct": 0.004, + "tp_pct": 0.006, + "min_hold_seconds": 180, + "max_hold_seconds": 1800, + "margin_per_trade": 100.0, + "leverage": 100, + "notional": 10000.0, + "rebate_rate": 0.9, + "train_period": "2020-01-01 ~ 2021-01-01", + "test_period": "2021-01-01 ~ 2022-01-01", + "kline_period": "5m", + "initial_balance": 10000.0, + "max_dd_target": 500.0 +} \ No newline at end of file diff --git a/models/ips.py b/models/ips.py new file mode 100644 index 0000000..d3e2ac7 --- /dev/null +++ b/models/ips.py @@ -0,0 +1,21 @@ +from peewee import * + +from models import db1 + + +class Ips(Model): + id = IntegerField(primary_key=True) + host = CharField(null=True) + port = CharField(null=True) + username = CharField(null=True) + password = CharField(null=True) + start = IntegerField(null=True) + country = CharField(null=True) + + class Meta: + database = db1 + table_name = 'ips' + + +# if __name__ == '__main__': +# Ips.create_table() diff --git a/models/mexc.py b/models/mexc.py new file mode 100644 index 0000000..54e05e9 --- /dev/null +++ b/models/mexc.py @@ -0,0 +1,57 @@ +from peewee import * + +from models import db + + +class Mexc1(Model): + id = IntegerField(primary_key=True) + open = FloatField(null=True) + high = FloatField(null=True) + low = FloatField(null=True) + close = FloatField(null=True) + + class Meta: + database = db + table_name = 'mexc_1' + + +class Mexc15(Model): + id = IntegerField(primary_key=True) + open = FloatField(null=True) + high = FloatField(null=True) + low = FloatField(null=True) + close = FloatField(null=True) + + class Meta: + database = db + table_name = 'mexc_15' + + +class Mexc30(Model): + id = IntegerField(primary_key=True) + open = FloatField(null=True) + high = FloatField(null=True) + low = FloatField(null=True) + close = FloatField(null=True) + + class Meta: + database = db + table_name = 'mexc_30' + + +class Mexc1Hour(Model): + id = IntegerField(primary_key=True) + open = FloatField(null=True) + high = FloatField(null=True) + low = FloatField(null=True) + close = FloatField(null=True) + + class Meta: + database = db + table_name = 'mexc_1_hour' + + +# 连接到数据库 +db.connect() +# 创建表（如果表不存在） +db.create_tables([Mexc1, Mexc15, Mexc30, Mexc1Hour]) diff --git a/models/weex.py b/models/weex.py new file mode 100644 index 0000000..7cfb894 --- /dev/null +++ b/models/weex.py @@ -0,0 +1,71 @@ +from peewee import * + +from models import db + + +class Weex15(Model): + id = IntegerField(primary_key=True) + open = FloatField(null=True) + high = FloatField(null=True) + low = FloatField(null=True) + close = FloatField(null=True) + + class Meta: + database = db + table_name = 'weex_15' + + +class Weex1(Model): + id = IntegerField(primary_key=True) + open = FloatField(null=True) + high = FloatField(null=True) + low = FloatField(null=True) + close = FloatField(null=True) + + class Meta: + database = db + table_name = 'weex_1' + + +class Weex1Hour(Model): + id = IntegerField(primary_key=True) + open = FloatField(null=True) + high = FloatField(null=True) + low = FloatField(null=True) + close = FloatField(null=True) + + class Meta: + database = db + table_name = 'weex_1_hour' + +class Weex30(Model): + id = IntegerField(primary_key=True) + open = FloatField(null=True) + high = FloatField(null=True) + low = FloatField(null=True) + close = FloatField(null=True) + + class Meta: + database = db + table_name = 'weex_30' + +class Weex30Copy(Model): + id = IntegerField(primary_key=True) + open = FloatField(null=True) + high = FloatField(null=True) + low = FloatField(null=True) + close = FloatField(null=True) + + class Meta: + database = db + table_name = 'weex_30_copy1' + + +# 连接到数据库 +db.connect() +# +# # 创建表（如果表不存在） +# db.create_tables([Weex15]) +db.create_tables([Weex30]) + + diff --git a/models/xstart.py b/models/xstart.py new file mode 100644 index 0000000..69dce38 --- /dev/null +++ b/models/xstart.py @@ -0,0 +1,22 @@ +from peewee import * + +from models import db1 +from models.ips import Ips + + +class Xstart(Model): + id = AutoField(primary_key=True) # 自增主键 + bit_id = CharField(null=True) + start = IntegerField(null=True) + x_id = IntegerField(null=True) + ip_id = IntegerField(null=True) + url_id = CharField(null=True) + + class Meta: + database = db1 # 所属数据库 + table_name = 'xstart' + + +if __name__ == '__main__': + + Xstart.create_table() \ No newline at end of file diff --git a/models/xtoken.py b/models/xtoken.py new file mode 100644 index 0000000..89041bf --- /dev/null +++ b/models/xtoken.py @@ -0,0 +1,24 @@ +from peewee import * +# 假设 db 已经在其他地方定义并连接到数据库 +from models import db1 + + +class XToken(Model): + id = AutoField(primary_key=True) # 自增主键 + hub_id = IntegerField(null=True) # hub_id 字段，整型，可为空 + start = IntegerField(null=True) # start 字段，整型，可为空 + account_start = IntegerField(null=True) # account_start 字段，整型，可为空 + user_name = CharField(max_length=255, null=True) # user_name 字段，最大长度 255，可为空 + password = CharField(max_length=255, null=True) # password 字段，最大长度 255，可为空 + email = CharField(max_length=255, null=True) # email 字段，最大长度 255，可为空 + two_fa = CharField(max_length=255, null=True) # 2fa 字段，由于 2fa 是 Python 中的无效标识符，这里使用 two_fa 替代，最大长度 255，可为空 + token = CharField(max_length=255, null=True) # token 字段，最大长度 255，可为空 + email_pwd = CharField(max_length=255, null=True) # token 字段，最大长度 255，可为空 + + class Meta: + database = db1 # 所属数据库 + table_name = 'x_token' # 表名 + + +if __name__ == '__main__': + XToken.create_table() diff --git a/抓取多周期K线.py b/抓取多周期K线.py new file mode 100644 index 0000000..64eeb78 --- /dev/null +++ b/抓取多周期K线.py @@ -0,0 +1,762 @@ +""" +BitMart 多周期K线数据抓取脚本 +支持同时获取 1分钟、3分钟、5分钟、15分钟、30分钟、1小时 K线数据 +支持秒级价格数据（通过成交记录API） +支持断点续传，从数据库最新/最早记录继续抓取 +""" + +import time +import datetime +from pathlib import Path +from loguru import logger +from peewee import * +from bitmart.api_contract import APIContract + +# 数据库配置（使用脚本所在项目目录下的 models） +DB_PATH = Path(__file__).parent / 'models' / 'database.db' +db = SqliteDatabase(str(DB_PATH)) + +# K线周期配置：step值 -> 表名后缀 +KLINE_CONFIGS = { + 1: '1m', # 1分钟 + 3: '3m', # 3分钟 + 5: '5m', # 5分钟 + 15: '15m', # 15分钟 + 30: '30m', # 30分钟 + 60: '1h', # 1小时 +} + + +class BitMartETHTrades(Model): + """成交记录模型（秒级/毫秒级原始数据）""" + id = BigIntegerField(primary_key=True) # 成交ID + timestamp = BigIntegerField(index=True) # 成交时间戳（毫秒） + price = FloatField() # 成交价格 + volume = FloatField() # 成交量 + side = IntegerField() # 方向: 1=买, -1=卖 + + class Meta: + database = db + table_name = 'bitmart_eth_trades' + + +class BitMartETHSecond(Model): + """秒级K线模型（由成交记录聚合而来）""" + id = BigIntegerField(primary_key=True) # 时间戳（毫秒，取整到秒） + open = FloatField(null=True) + high = FloatField(null=True) + low = FloatField(null=True) + close = FloatField(null=True) + volume = FloatField(null=True) + trade_count = IntegerField(null=True) # 该秒内成交笔数 + + class Meta: + database = db + table_name = 'bitmart_eth_1s' + + +def create_kline_model(step: int): + """ + 动态创建K线数据模型 + :param step: K线周期（分钟） + :return: Model类 + """ + suffix = KLINE_CONFIGS.get(step, f'{step}m') + tbl_name = f'bitmart_eth_{suffix}' + + # 使用 type() 动态创建类，避免闭包问题 + attrs = { + 'id': BigIntegerField(primary_key=True), + 'open': FloatField(null=True), + 'high': FloatField(null=True), + 'low': FloatField(null=True), + 'close': FloatField(null=True), + } + + # 创建 Meta 类 + meta_attrs = { + 'database': db, + 'table_name': tbl_name, + } + Meta = type('Meta', (), meta_attrs) + attrs['Meta'] = Meta + + # 动态创建 Model 类 + model_name = f'BitMartETH{suffix.upper()}' + KlineModel = type(model_name, (Model,), attrs) + + return KlineModel + + +class BitMartMultiKlineCollector: + """多周期K线数据抓取器""" + + def __init__(self): + self.api_key = "a0fb7b98464fd9bcce67e7c519d58ec10d0c38a8" + self.secret_key = "4eaeba78e77aeaab1c2027f846a276d164f264a44c2c1bb1c5f3be50c8de1ca5" + self.memo = "数据抓取" + self.contract_symbol = "ETHUSDT" + self.contractAPI = APIContract(self.api_key, self.secret_key, self.memo, timeout=(5, 15)) + + # 存储各周期的模型 + self.models = {} + + # 初始化数据库连接和表 + self._init_database() + + def _init_database(self): + """初始化数据库，创建所有周期的表""" + DB_PATH.parent.mkdir(parents=True, exist_ok=True) + db.connect(reuse_if_open=True) + + for step in KLINE_CONFIGS.keys(): + model = create_kline_model(step) + self.models[step] = model + # 创建表（如果不存在） + db.create_tables([model], safe=True) + logger.info(f"初始化表: {model._meta.table_name}") + + # 创建成交记录表和秒级K线表 + db.create_tables([BitMartETHTrades, BitMartETHSecond], safe=True) + logger.info(f"初始化表: bitmart_eth_trades (成交记录)") + logger.info(f"初始化表: bitmart_eth_1s (秒级K线)") + + def get_db_time_range(self, step: int): + """ + 获取数据库中已有数据的时间范围 + :param step: K线周期 + :return: (earliest_ts, latest_ts) 毫秒时间戳，无数据返回 (None, None) + """ + model = self.models.get(step) + if not model: + return None, None + + try: + # 获取最早记录 + earliest = model.select(fn.MIN(model.id)).scalar() + # 获取最新记录 + latest = model.select(fn.MAX(model.id)).scalar() + return earliest, latest + except Exception as e: + logger.error(f"查询数据库时间范围异常: {e}") + return None, None + + def get_klines(self, step: int, start_time: int, end_time: int, max_retries: int = 3): + """ + 获取K线数据（带重试） + :param step: K线周期（分钟） + :param start_time: 开始时间戳（秒级） + :param end_time: 结束时间戳（秒级） + :param max_retries: 最大重试次数 + :return: K线数据列表 + """ + for attempt in range(max_retries): + try: + start_time = int(start_time) + end_time = int(end_time) + + response = self.contractAPI.get_kline( + contract_symbol=self.contract_symbol, + step=step, + start_time=start_time, + end_time=end_time + )[0] + + if response['code'] != 1000: + logger.warning(f"API返回错误 (尝试 {attempt+1}/{max_retries}): {response}") + if attempt < max_retries - 1: + time.sleep(1) + continue + return [] + + klines = response.get('data', []) + formatted = [] + for k in klines: + timestamp_ms = int(k["timestamp"]) * 1000 + formatted.append({ + 'id': timestamp_ms, + 'open': float(k["open_price"]), + 'high': float(k["high_price"]), + 'low': float(k["low_price"]), + 'close': float(k["close_price"]) + }) + + formatted.sort(key=lambda x: x['id']) + return formatted + + except Exception as e: + logger.error(f"获取K线异常 (尝试 {attempt+1}/{max_retries}): {e}") + if attempt < max_retries - 1: + time.sleep(2) + continue + return [] + + return [] + + def save_klines(self, step: int, klines: list): + """ + 保存K线数据到数据库 + :param step: K线周期 + :param klines: K线数据列表 + :return: 新保存的数量 + """ + model = self.models.get(step) + if not model: + logger.error(f"未找到 {step}分钟 的数据模型") + return 0 + + new_count = 0 + for kline in klines: + try: + _, created = model.get_or_create( + id=kline['id'], + defaults={ + 'open': kline['open'], + 'high': kline['high'], + 'low': kline['low'], + 'close': kline['close'], + } + ) + if created: + new_count += 1 + except Exception as e: + logger.error(f"保存K线数据失败 {kline['id']}: {e}") + + return new_count + + def get_batch_seconds(self, step: int): + """根据周期获取合适的批次大小""" + if step == 1: + return 3600 * 4 # 1分钟: 每次4小时 + elif step == 3: + return 3600 * 8 # 3分钟: 每次8小时 + elif step == 5: + return 3600 * 12 # 5分钟: 每次12小时 + elif step == 15: + return 3600 * 24 # 15分钟: 每次1天 + elif step == 30: + return 3600 * 48 # 30分钟: 每次2天 + else: + return 3600 * 72 # 1小时: 每次3天 + + def collect_period_range(self, step: int, target_start: int, target_end: int): + """ + 抓取指定时间范围的K线数据（支持断点续传） + :param step: K线周期（分钟） + :param target_start: 目标开始时间戳（秒） + :param target_end: 目标结束时间戳（秒） + :return: 保存的总数量 + """ + suffix = KLINE_CONFIGS.get(step, f'{step}m') + batch_seconds = self.get_batch_seconds(step) + + # 获取数据库已有数据范围 + db_earliest, db_latest = self.get_db_time_range(step) + + if db_earliest and db_latest: + db_earliest_sec = db_earliest // 1000 + db_latest_sec = db_latest // 1000 + logger.info(f"[{suffix}] 数据库已有数据: " + f"{time.strftime('%Y-%m-%d %H:%M', time.localtime(db_earliest_sec))} ~ " + f"{time.strftime('%Y-%m-%d %H:%M', time.localtime(db_latest_sec))}") + else: + db_earliest_sec = None + db_latest_sec = None + logger.info(f"[{suffix}] 数据库暂无数据") + + total_saved = 0 + + # === 第一阶段：向前抓取历史数据（从数据库最早记录向前，直到 target_start）=== + if db_earliest_sec: + backward_end = db_earliest_sec + else: + backward_end = target_end + + if backward_end > target_start: + logger.info(f"[{suffix}] === 开始向前抓取历史数据 ===") + total_backward = backward_end - target_start + + current_end = backward_end + fail_count = 0 + max_fail = 5 + + while current_end > target_start and fail_count < max_fail: + current_start = max(current_end - batch_seconds, target_start) + + # 计算进度 + progress = (backward_end - current_end) / total_backward * 100 if total_backward > 0 else 0 + start_str = time.strftime('%Y-%m-%d %H:%M', time.localtime(current_start)) + end_str = time.strftime('%Y-%m-%d %H:%M', time.localtime(current_end)) + + klines = self.get_klines(step, current_start, current_end) + if klines: + saved = self.save_klines(step, klines) + total_saved += saved + logger.info(f"[{suffix}] ← 历史 {start_str} ~ {end_str} | " + f"获取 {len(klines)} 条, 新增 {saved} 条 | 进度 {progress:.1f}%") + fail_count = 0 + else: + fail_count += 1 + logger.warning(f"[{suffix}] ← 历史 {start_str} 无数据 (连续失败 {fail_count}/{max_fail})") + if fail_count >= max_fail: + earliest_date = time.strftime('%Y-%m-%d', time.localtime(current_end)) + logger.warning(f"[{suffix}] 已达到API历史数据限制，最早可获取: {earliest_date}") + break + + current_end = current_start + time.sleep(0.3) + + # === 第二阶段：向后抓取最新数据（从数据库最新记录向后，直到 target_end）=== + if db_latest_sec: + forward_start = db_latest_sec + else: + # 如果没有数据，从第一阶段结束的地方开始 + forward_start = target_start + + if forward_start < target_end: + logger.info(f"[{suffix}] === 开始向后抓取最新数据 ===") + total_forward = target_end - forward_start + + current_start = forward_start + fail_count = 0 + max_fail = 3 + + while current_start < target_end and fail_count < max_fail: + current_end = min(current_start + batch_seconds, target_end) + + # 计算进度 + progress = (current_start - forward_start) / total_forward * 100 if total_forward > 0 else 0 + start_str = time.strftime('%Y-%m-%d %H:%M', time.localtime(current_start)) + end_str = time.strftime('%Y-%m-%d %H:%M', time.localtime(current_end)) + + klines = self.get_klines(step, current_start, current_end) + if klines: + saved = self.save_klines(step, klines) + total_saved += saved + logger.info(f"[{suffix}] → 最新 {start_str} ~ {end_str} | " + f"获取 {len(klines)} 条, 新增 {saved} 条 | 进度 {progress:.1f}%") + fail_count = 0 + else: + fail_count += 1 + logger.warning(f"[{suffix}] → 最新 {start_str} 无数据 (失败 {fail_count}/{max_fail})") + + current_start = current_end + time.sleep(0.3) + + # 统计最终数据范围 + final_earliest, final_latest = self.get_db_time_range(step) + if final_earliest and final_latest: + logger.success(f"[{suffix}] 抓取完成！本次新增 {total_saved} 条 | 数据范围: " + f"{time.strftime('%Y-%m-%d', time.localtime(final_earliest//1000))} ~ " + f"{time.strftime('%Y-%m-%d', time.localtime(final_latest//1000))}") + else: + logger.success(f"[{suffix}] 抓取完成！本次新增 {total_saved} 条") + + return total_saved + + def collect_from_date(self, start_date: str, periods: list = None): + """ + 从指定日期抓取到当前时间 + :param start_date: 起始日期 'YYYY-MM-DD' + :param periods: 要抓取的周期列表，如 [1, 5, 15]，默认全部 + """ + if periods is None: + periods = list(KLINE_CONFIGS.keys()) + + # 计算时间范围 + start_dt = datetime.datetime.strptime(start_date, '%Y-%m-%d') + target_start = int(start_dt.timestamp()) + target_end = int(time.time()) + + start_str = start_dt.strftime('%Y-%m-%d') + end_str = datetime.datetime.now().strftime('%Y-%m-%d %H:%M') + + logger.info(f"{'='*60}") + logger.info(f"目标时间范围: {start_str} ~ {end_str}") + logger.info(f"抓取周期: {[KLINE_CONFIGS[p] for p in periods]}") + logger.info(f"{'='*60}") + + results = {} + for step in periods: + if step not in KLINE_CONFIGS: + logger.warning(f"不支持的周期: {step}分钟，跳过") + continue + + logger.info(f"\n{'='*60}") + logger.info(f"开始抓取 {KLINE_CONFIGS[step]} K线") + logger.info(f"{'='*60}") + + saved = self.collect_period_range(step, target_start, target_end) + results[KLINE_CONFIGS[step]] = saved + + time.sleep(1) # 不同周期之间间隔 + + # 打印总结 + logger.info(f"\n{'='*60}") + logger.info("所有周期抓取完成！统计：") + for period, count in results.items(): + logger.info(f" {period}: 新增 {count} 条") + logger.info(f"{'='*60}") + + return results + + def get_stats(self): + """获取各周期数据统计""" + logger.info(f"\n{'='*60}") + logger.info("数据库统计:") + logger.info(f"{'='*60}") + + for step, model in self.models.items(): + suffix = KLINE_CONFIGS.get(step, f'{step}m') + try: + count = model.select().count() + earliest, latest = self.get_db_time_range(step) + if earliest and latest: + earliest_str = time.strftime('%Y-%m-%d %H:%M', time.localtime(earliest//1000)) + latest_str = time.strftime('%Y-%m-%d %H:%M', time.localtime(latest//1000)) + logger.info(f" {suffix:>4}: {count:>8} 条 | {earliest_str} ~ {latest_str}") + else: + logger.info(f" {suffix:>4}: {count:>8} 条") + except Exception as e: + logger.error(f" {suffix}: 查询失败 - {e}") + + # 成交记录统计 + try: + trades_count = BitMartETHTrades.select().count() + if trades_count > 0: + earliest_trade = BitMartETHTrades.select(fn.MIN(BitMartETHTrades.timestamp)).scalar() + latest_trade = BitMartETHTrades.select(fn.MAX(BitMartETHTrades.timestamp)).scalar() + earliest_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(earliest_trade//1000)) + latest_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(latest_trade//1000)) + logger.info(f"trades: {trades_count:>8} 条 | {earliest_str} ~ {latest_str}") + else: + logger.info(f"trades: {trades_count:>8} 条") + except Exception as e: + logger.error(f"trades: 查询失败 - {e}") + + # 秒级K线统计 + try: + second_count = BitMartETHSecond.select().count() + if second_count > 0: + earliest_sec = BitMartETHSecond.select(fn.MIN(BitMartETHSecond.id)).scalar() + latest_sec = BitMartETHSecond.select(fn.MAX(BitMartETHSecond.id)).scalar() + earliest_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(earliest_sec//1000)) + latest_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(latest_sec//1000)) + logger.info(f" 1s: {second_count:>8} 条 | {earliest_str} ~ {latest_str}") + else: + logger.info(f" 1s: {second_count:>8} 条") + except Exception as e: + logger.error(f" 1s: 查询失败 - {e}") + + logger.info(f"{'='*60}") + + # ==================== 秒级数据相关方法 ==================== + + def get_trades(self, limit: int = 100): + """ + 获取最近成交记录 + :param limit: 获取条数 + :return: 成交记录列表 + """ + try: + response = self.contractAPI.get_trades( + contract_symbol=self.contract_symbol, + )[0] + + if response['code'] != 1000: + logger.error(f"获取成交记录失败: {response}") + return [] + + trades = response.get('data', {}).get('trades', []) + formatted = [] + for t in trades: + formatted.append({ + 'id': int(t.get('trade_id', 0)), + 'timestamp': int(t.get('create_time', 0)), + 'price': float(t.get('deal_price', 0)), + 'volume': float(t.get('deal_vol', 0)), + 'side': int(t.get('way', 0)), + }) + + return formatted + except Exception as e: + logger.error(f"获取成交记录异常: {e}") + return [] + + def save_trades(self, trades: list): + """保存成交记录到数据库""" + new_count = 0 + for trade in trades: + try: + _, created = BitMartETHTrades.get_or_create( + id=trade['id'], + defaults={ + 'timestamp': trade['timestamp'], + 'price': trade['price'], + 'volume': trade['volume'], + 'side': trade['side'], + } + ) + if created: + new_count += 1 + except Exception as e: + pass # 忽略重复数据 + return new_count + + def collect_trades_realtime(self, duration_seconds: int = 3600, interval: float = 0.3): + """ + 实时持续采集成交记录（秒级数据源） + :param duration_seconds: 采集时长（秒），默认1小时 + :param interval: 采集间隔（秒），默认0.3秒 + """ + logger.info(f"{'='*60}") + logger.info(f"开始实时采集成交记录") + logger.info(f"时长: {duration_seconds}秒 ({duration_seconds/3600:.1f}小时)") + logger.info(f"间隔: {interval}秒") + logger.info(f"{'='*60}") + + start_time = time.time() + end_time = start_time + duration_seconds + total_saved = 0 + batch_count = 0 + + while time.time() < end_time: + trades = self.get_trades(limit=100) + if trades: + saved = self.save_trades(trades) + total_saved += saved + batch_count += 1 + + # 每10批显示一次进度 + if batch_count % 10 == 0: + elapsed = time.time() - start_time + remaining = end_time - time.time() + latest = trades[-1] + ts_str = datetime.datetime.fromtimestamp( + latest['timestamp']/1000 + ).strftime('%H:%M:%S') + logger.info(f"[{ts_str}] 价格: {latest['price']:.2f} | " + f"本批新增: {saved} | 累计: {total_saved} | " + f"剩余: {remaining/60:.1f}分钟") + + time.sleep(interval) + + logger.success(f"采集完成！共新增 {total_saved} 条成交记录") + + # 自动聚合为秒级K线 + logger.info("正在将成交记录聚合为秒级K线...") + self.aggregate_trades_to_seconds() + + return total_saved + + def aggregate_trades_to_seconds(self, start_ts: int = None, end_ts: int = None): + """ + 将成交记录聚合为秒级K线数据 + :param start_ts: 开始时间戳（毫秒），默认全部 + :param end_ts: 结束时间戳（毫秒），默认全部 + :return: 聚合的秒级K线数量 + """ + # 构建查询 + query = BitMartETHTrades.select().order_by(BitMartETHTrades.timestamp) + if start_ts: + query = query.where(BitMartETHTrades.timestamp >= start_ts) + if end_ts: + query = query.where(BitMartETHTrades.timestamp <= end_ts) + + # 按秒聚合 + second_data = {} + trade_count = 0 + + for trade in query: + trade_count += 1 + # 取整到秒（毫秒时间戳） + second_ts = (trade.timestamp // 1000) * 1000 + + if second_ts not in second_data: + second_data[second_ts] = { + 'open': trade.price, + 'high': trade.price, + 'low': trade.price, + 'close': trade.price, + 'volume': trade.volume, + 'trade_count': 1 + } + else: + second_data[second_ts]['high'] = max(second_data[second_ts]['high'], trade.price) + second_data[second_ts]['low'] = min(second_data[second_ts]['low'], trade.price) + second_data[second_ts]['close'] = trade.price + second_data[second_ts]['volume'] += trade.volume + second_data[second_ts]['trade_count'] += 1 + + # 保存到数据库 + saved_count = 0 + for ts, ohlc in second_data.items(): + try: + BitMartETHSecond.insert( + id=ts, + open=ohlc['open'], + high=ohlc['high'], + low=ohlc['low'], + close=ohlc['close'], + volume=ohlc['volume'], + trade_count=ohlc['trade_count'] + ).on_conflict( + conflict_target=[BitMartETHSecond.id], + update={ + BitMartETHSecond.open: ohlc['open'], + BitMartETHSecond.high: ohlc['high'], + BitMartETHSecond.low: ohlc['low'], + BitMartETHSecond.close: ohlc['close'], + BitMartETHSecond.volume: ohlc['volume'], + BitMartETHSecond.trade_count: ohlc['trade_count'], + } + ).execute() + saved_count += 1 + except Exception as e: + logger.error(f"保存秒级K线失败 {ts}: {e}") + + logger.success(f"聚合完成！{trade_count} 条成交记录 → {saved_count} 条秒级K线") + return saved_count + + def get_second_klines(self, start_ts: int = None, end_ts: int = None): + """ + 获取秒级K线数据 + :param start_ts: 开始时间戳（毫秒） + :param end_ts: 结束时间戳（毫秒） + :return: 秒级K线列表 + """ + query = BitMartETHSecond.select().order_by(BitMartETHSecond.id) + if start_ts: + query = query.where(BitMartETHSecond.id >= start_ts) + if end_ts: + query = query.where(BitMartETHSecond.id <= end_ts) + + return [{ + 'timestamp': k.id, + 'open': k.open, + 'high': k.high, + 'low': k.low, + 'close': k.close, + 'volume': k.volume, + 'trade_count': k.trade_count + } for k in query] + + def aggregate_trades_custom(self, interval_ms: int = 100, start_ts: int = None, end_ts: int = None): + """ + 将成交记录聚合为自定义毫秒级K线数据（不保存到数据库，直接返回） + :param interval_ms: 聚合周期（毫秒），如 100=100ms, 500=500ms, 1000=1秒 + :param start_ts: 开始时间戳（毫秒） + :param end_ts: 结束时间戳（毫秒） + :return: K线列表 [{'timestamp', 'open', 'high', 'low', 'close', 'volume', 'trade_count'}, ...] + """ + # 构建查询 + query = BitMartETHTrades.select().order_by(BitMartETHTrades.timestamp) + if start_ts: + query = query.where(BitMartETHTrades.timestamp >= start_ts) + if end_ts: + query = query.where(BitMartETHTrades.timestamp <= end_ts) + + # 按指定间隔聚合 + interval_data = {} + trade_count = 0 + + for trade in query: + trade_count += 1 + # 取整到指定间隔 + interval_ts = (trade.timestamp // interval_ms) * interval_ms + + if interval_ts not in interval_data: + interval_data[interval_ts] = { + 'open': trade.price, + 'high': trade.price, + 'low': trade.price, + 'close': trade.price, + 'volume': trade.volume, + 'trade_count': 1 + } + else: + interval_data[interval_ts]['high'] = max(interval_data[interval_ts]['high'], trade.price) + interval_data[interval_ts]['low'] = min(interval_data[interval_ts]['low'], trade.price) + interval_data[interval_ts]['close'] = trade.price + interval_data[interval_ts]['volume'] += trade.volume + interval_data[interval_ts]['trade_count'] += 1 + + # 转换为列表 + result = [] + for ts, ohlc in sorted(interval_data.items()): + result.append({ + 'timestamp': ts, + 'datetime': datetime.datetime.fromtimestamp(ts/1000).strftime('%Y-%m-%d %H:%M:%S.%f')[:-3], + 'open': ohlc['open'], + 'high': ohlc['high'], + 'low': ohlc['low'], + 'close': ohlc['close'], + 'volume': ohlc['volume'], + 'trade_count': ohlc['trade_count'] + }) + + logger.info(f"聚合完成: {trade_count} 条成交记录 → {len(result)} 条 {interval_ms}ms K线") + return result + + def get_raw_trades(self, start_ts: int = None, end_ts: int = None, limit: int = None): + """ + 获取原始成交记录（逐笔数据，毫秒级） + :param start_ts: 开始时间戳（毫秒） + :param end_ts: 结束时间戳（毫秒） + :param limit: 最大返回条数 + :return: 成交记录列表 + """ + query = BitMartETHTrades.select().order_by(BitMartETHTrades.timestamp) + if start_ts: + query = query.where(BitMartETHTrades.timestamp >= start_ts) + if end_ts: + query = query.where(BitMartETHTrades.timestamp <= end_ts) + if limit: + query = query.limit(limit) + + return [{ + 'id': t.id, + 'timestamp': t.timestamp, + 'datetime': datetime.datetime.fromtimestamp(t.timestamp/1000).strftime('%Y-%m-%d %H:%M:%S.%f')[:-3], + 'price': t.price, + 'volume': t.volume, + 'side': '买' if t.side == 1 else '卖' + } for t in query] + + def close(self): + """关闭数据库连接""" + if not db.is_closed(): + db.close() + + +if __name__ == '__main__': + collector = BitMartMultiKlineCollector() + + try: + # 查看当前数据统计 + collector.get_stats() + + # ============ 选择要执行的任务 ============ + + # 任务1: 抓取K线数据（1分钟~1小时周期） + # 从 2025-01-01 抓取到当前时间（支持断点续传） + collector.collect_from_date( + start_date='2010-01-01', + periods=[1, 3, 5, 15, 30, 60] # 所有周期 + ) + + # 任务2: 实时采集秒级数据（成交记录） + # 注意: 秒级数据只能实时采集，无法获取历史 + # collector.collect_trades_realtime( + # duration_seconds=3600, # 采集1小时 + # interval=0.3 # 每0.3秒请求一次 + # ) + + # 任务3: 将已采集的成交记录聚合为秒级K线 + # collector.aggregate_trades_to_seconds() + + # 再次查看统计 + collector.get_stats() + + finally: + collector.close() diff --git a/训练AI策略_ETH合约.py b/训练AI策略_ETH合约.py new file mode 100644 index 0000000..3015809 --- /dev/null +++ b/训练AI策略_ETH合约.py @@ -0,0 +1,989 @@ +""" +ETH 合约 AI 策略训练 + 回测 + +规则: + - 标的: ETH 合约 (BitMart 1分钟K线) + - 同一时间仅 1 个仓位 (多或空) + - 每笔固定: 100U 保证金 × 100 倍 = 10000U 名义价值 + - 手续费 90% 返佣，目标: 月均净利 >= 1000 USDT + +流程: + 1. 训练: 使用 2020 年全年数据训练 LightGBM（特征含多种可调参数指标） + 2. 回测: 使用 2021 年全年数据做严格样本外真实回测（模型从未见过 2021 数据） + 3. 参数搜索在「2021 年月均净利」上选最佳并保存 +""" + +import datetime +import json +import sqlite3 +import time as _time +from pathlib import Path +from collections import defaultdict +import numpy as np +import pandas as pd +import lightgbm as lgb + +import warnings +warnings.filterwarnings('ignore') + +# 常量：ETH 合约，100U 保证金 100 倍 +MARGIN_PER_TRADE = 100.0 # USDT +LEVERAGE = 100 +NOTIONAL_PER_TRADE = MARGIN_PER_TRADE * LEVERAGE # 10000 USDT +TAKER_FEE_RATE = 0.0006 +REBATE_RATE = 0.90 +# 手续费：先扣全额，返佣次日 8 点才到账，回撤按「可用资金」算 +FULL_FEE_PER_TRADE = NOTIONAL_PER_TRADE * TAKER_FEE_RATE * 2 +REBATE_PER_TRADE = FULL_FEE_PER_TRADE * REBATE_RATE +NET_FEE_PER_TRADE = FULL_FEE_PER_TRADE - REBATE_PER_TRADE # 净手续费 + +# 本金与风控目标 +INITIAL_BALANCE = 10000.0 # 本金 10000 USDT +MAX_DD_TARGET = 500.0 # 目标：可用资金最大回撤 ≤ 500 USDT + +# 训练 / 真实回测 时间范围（左闭右开） +TRAIN_START = '2020-01-01' +TRAIN_END_EXCLUSIVE = '2021-01-01' # 训练数据: [2020-01-01, 2021-01-01) = 2020 全年 +TEST_START = '2021-01-01' +TEST_END_EXCLUSIVE = '2022-01-01' # 回测数据: [2021-01-01, 2022-01-01) = 2021 全年 + + +def get_db_path(): + return Path(__file__).parent / 'models' / 'database.db' + + +# 多周期：与 抓取多周期K线.py 一致，表名 bitmart_eth_{suffix} +PERIOD_TABLES = { + '1m': 'bitmart_eth_1m', + '3m': 'bitmart_eth_3m', + '5m': 'bitmart_eth_5m', + '15m': 'bitmart_eth_15m', + '30m': 'bitmart_eth_30m', + '1h': 'bitmart_eth_1h', +} + + +def _normalize_period(period) -> str: + """5 -> '5m', 15 -> '15m', '5' -> '5m', '15m' -> '15m'""" + if isinstance(period, int): + return f'{period}m' if period != 60 else '1h' + s = str(period).strip().lower() + if s in PERIOD_TABLES: + return s + if s.endswith('m'): + return s + if s == '60' or s == '1h': + return '1h' + return f'{s}m' if s.isdigit() else s + + +# ==================== 数据加载 ==================== +def load_klines(start_date: str = '2025-01-01', end_date: str = '2026-02-01', period: str = '1m'): + """从 SQLite 读取指定周期 K 线。period: '1m'|'3m'|'5m'|'15m'|'30m'|'1h' 或 1|3|5|15|30|60""" + period = _normalize_period(period) + table = PERIOD_TABLES.get(period) + if not table: + raise ValueError(f"不支持的周期: {period}，可选: {list(PERIOD_TABLES.keys())}") + db = get_db_path() + if not db.exists(): + raise FileNotFoundError(f"数据库不存在: {db}，请先运行 抓取多周期K线.py 拉取数据") + start_ts = int(datetime.datetime.strptime(start_date, '%Y-%m-%d').timestamp()) * 1000 + end_ts = int(datetime.datetime.strptime(end_date, '%Y-%m-%d').timestamp()) * 1000 + conn = sqlite3.connect(str(db)) + df = pd.read_sql_query( + f"SELECT id as ts, open, high, low, close FROM {table} " + "WHERE id >= ? AND id < ? ORDER BY id", + conn, params=(start_ts, end_ts)) + conn.close() + if len(df) == 0: + raise FileNotFoundError(f"表 {table} 中无 {start_date}~{end_date} 数据，请先抓取该周期 K 线") + df['datetime'] = pd.to_datetime(df['ts'], unit='ms') + df.set_index('datetime', inplace=True) + return df + + +# ==================== 指标参数（类布林带/均值回归，均可训练优化） ==================== +def default_indicator_params(): + """默认指标参数，均可被搜索优化。含：布林、肯特纳、唐奇安、随机、CCI、威廉、RSI、ATR带、Z-Score、线性回归通道、中位数带、百分位排名、Chandelier、标准差带、Elder 射线等""" + return { + 'bb_period': 20, + 'bb_std': 2.0, + 'keltner_period': 20, + 'keltner_atr_mult': 2.0, + 'donchian_period': 20, + 'stoch_k_period': 14, + 'stoch_d_period': 3, + 'cci_period': 20, + 'willr_period': 14, + 'rsi_period': 14, + 'atr_band_period': 20, + 'atr_band_mult': 2.0, + # 均值回归类新增 + 'zscore_period': 20, + 'lr_period': 20, + 'lr_std_mult': 2.0, + 'median_band_period': 20, + 'pct_rank_period': 20, + 'chandelier_period': 22, + 'chandelier_mult': 3.0, + 'std_band_period': 14, + 'std_band_mult': 2.0, + 'elder_period': 13, + } + + +# ==================== 特征工程（所有带带/通道类指标参数可调） ==================== +def add_features(df: pd.DataFrame, ind: dict = None) -> pd.DataFrame: + """生成特征。ind 为指标参数字典，缺省用 default_indicator_params()""" + if ind is None: + ind = default_indicator_params() + c = df['close'] + h = df['high'] + l = df['low'] + o = df['open'] + cp = c.replace(0, np.nan) + + # --- 布林带 --- + bp = ind['bb_period'] + bstd = ind['bb_std'] + mid = c.rolling(bp).mean() + std = c.rolling(bp).std() + df['bb_upper'] = mid + bstd * std + df['bb_lower'] = mid - bstd * std + df['bb_mid'] = mid + df['bb_pct'] = (c - df['bb_lower']) / (df['bb_upper'] - df['bb_lower']).replace(0, np.nan) + df['bb_width'] = (df['bb_upper'] - df['bb_lower']) / mid.replace(0, np.nan) + + # --- 肯特纳通道 (Keltner) --- + kp = ind['keltner_period'] + katr = ind['keltner_atr_mult'] + tr = pd.concat([h - l, (h - c.shift(1)).abs(), (l - c.shift(1)).abs()], axis=1).max(axis=1) + atr_k = tr.rolling(kp).mean() + k_mid = c.ewm(span=kp, adjust=False).mean() + df['keltner_upper'] = k_mid + katr * atr_k + df['keltner_lower'] = k_mid - katr * atr_k + df['keltner_mid'] = k_mid + df['keltner_pct'] = (c - df['keltner_lower']) / (df['keltner_upper'] - df['keltner_lower']).replace(0, np.nan) + df['keltner_width'] = (df['keltner_upper'] - df['keltner_lower']) / k_mid.replace(0, np.nan) + + # --- 唐奇安通道 (Donchian) --- + dp = ind['donchian_period'] + du = h.rolling(dp).max() + dd = l.rolling(dp).min() + dm = (du + dd) / 2 + df['donchian_upper'] = du + df['donchian_lower'] = dd + df['donchian_mid'] = dm + df['donchian_pct'] = (c - dd) / (du - dd).replace(0, np.nan) + df['donchian_width'] = (du - dd) / dm.replace(0, np.nan) + + # --- 随机指标 (Stochastic) --- + sk, sd = ind['stoch_k_period'], ind['stoch_d_period'] + low_k = l.rolling(sk).min() + high_k = h.rolling(sk).max() + df['stoch_k'] = (c - low_k) / (high_k - low_k).replace(0, np.nan) * 100 + df['stoch_d'] = df['stoch_k'].rolling(sd).mean() + + # --- CCI --- + cci_p = ind['cci_period'] + typical = (h + l + c) / 3 + cci_m = typical.rolling(cci_p).mean() + cci_s = typical.rolling(cci_p).std() + df['cci'] = (typical - cci_m) / (0.015 * cci_s.replace(0, np.nan)) + + # --- 威廉 %R --- + wp = ind['willr_period'] + high_w = h.rolling(wp).max() + low_w = l.rolling(wp).min() + df['willr'] = -100 * (high_w - c) / (high_w - low_w).replace(0, np.nan) + + # --- RSI --- + rp = ind['rsi_period'] + delta = c.diff() + gain = delta.clip(lower=0) + loss = (-delta).clip(lower=0) + avg_gain = gain.rolling(rp).mean() + avg_loss = loss.rolling(rp).mean() + rs = avg_gain / avg_loss.replace(0, np.nan) + df['rsi'] = 100 - 100 / (1 + rs) + + # --- ATR 带 (中轨 SMA，带宽 ATR 倍数) --- + abp, abm = ind['atr_band_period'], ind['atr_band_mult'] + atr_ab = tr.rolling(abp).mean() + ab_mid = c.rolling(abp).mean() + df['atr_band_upper'] = ab_mid + abm * atr_ab + df['atr_band_lower'] = ab_mid - abm * atr_ab + df['atr_band_mid'] = ab_mid + df['atr_band_pct'] = (c - df['atr_band_lower']) / (df['atr_band_upper'] - df['atr_band_lower']).replace(0, np.nan) + df['atr_band_width'] = (df['atr_band_upper'] - df['atr_band_lower']) / ab_mid.replace(0, np.nan) + + # --- Z-Score 均值回归（价格偏离均值的标准差倍数）--- + zp = ind['zscore_period'] + z_mid = c.rolling(zp).mean() + z_std = c.rolling(zp).std() + df['zscore'] = (c - z_mid) / z_std.replace(0, np.nan) + df['zscore_abs'] = df['zscore'].abs() + + # --- 线性回归通道 (Linear Regression Channel)，向量化实现 --- + lrp = ind['lr_period'] + lr_mult = ind['lr_std_mult'] + sum_x = lrp * (lrp - 1) / 2.0 + sum_x2 = lrp * (lrp - 1) * (2 * lrp - 1) / 6.0 + s_xy = sum(c.shift(j) * j for j in range(lrp)) + s_y = c.rolling(lrp).sum() + denom = lrp * sum_x2 - sum_x * sum_x + slope = (lrp * s_xy - sum_x * s_y) / (denom or 1e-10) + intercept = s_y / lrp - slope * (lrp - 1) / 2.0 + lr_mid = intercept + slope * (lrp - 1) + lr_resid = c.rolling(lrp).std() + df['lr_upper'] = lr_mid + lr_mult * lr_resid + df['lr_lower'] = lr_mid - lr_mult * lr_resid + df['lr_mid'] = lr_mid + df['lr_pct'] = (c - df['lr_lower']) / (df['lr_upper'] - df['lr_lower']).replace(0, np.nan) + df['lr_width'] = (df['lr_upper'] - df['lr_lower']) / lr_mid.replace(0, np.nan) + + # --- 中位数带 (Price vs Median，稳健均值回归) --- + mdp = ind['median_band_period'] + med = c.rolling(mdp).median() + mstd = c.rolling(mdp).std() + df['median_band_mid'] = med + df['price_vs_median'] = (c - med) / mstd.replace(0, np.nan) + df['median_band_upper'] = med + 2 * mstd + df['median_band_lower'] = med - 2 * mstd + df['median_band_pct'] = (c - df['median_band_lower']) / (df['median_band_upper'] - df['median_band_lower']).replace(0, np.nan) + + # --- 百分位排名 (Percent Rank，0~1)，向量化：价格在区间内位置 --- + prp = ind['pct_rank_period'] + rmin = c.rolling(prp).min() + rmax = c.rolling(prp).max() + df['pct_rank'] = (c - rmin) / (rmax - rmin).replace(0, np.nan) + + # --- Chandelier Exit 通道 --- + cep, cem = ind['chandelier_period'], ind['chandelier_mult'] + atr_ce = tr.rolling(cep).mean() + high_max = h.rolling(cep).max() + low_min = l.rolling(cep).min() + df['chandelier_upper'] = high_max - cem * atr_ce + df['chandelier_lower'] = low_min + cem * atr_ce + df['chandelier_mid'] = (df['chandelier_upper'] + df['chandelier_lower']) / 2 + df['chandelier_pct'] = (c - df['chandelier_lower']) / (df['chandelier_upper'] - df['chandelier_lower']).replace(0, np.nan) + df['chandelier_width'] = (df['chandelier_upper'] - df['chandelier_lower']) / df['chandelier_mid'].replace(0, np.nan) + + # --- 标准差带 (Std Band，与 BB 不同周期/倍数可组合) --- + sbp, sbm = ind['std_band_period'], ind['std_band_mult'] + sb_mid = c.rolling(sbp).mean() + sb_std = c.rolling(sbp).std() + df['std_band_upper'] = sb_mid + sbm * sb_std + df['std_band_lower'] = sb_mid - sbm * sb_std + df['std_band_mid'] = sb_mid + df['std_band_pct'] = (c - df['std_band_lower']) / (df['std_band_upper'] - df['std_band_lower']).replace(0, np.nan) + df['std_band_width'] = (df['std_band_upper'] - df['std_band_lower']) / sb_mid.replace(0, np.nan) + + # --- Elder 射线 (Elder Ray，价格与均线的偏离) --- + ep = ind['elder_period'] + ema_elder = c.ewm(span=ep, adjust=False).mean() + df['elder_bull'] = (h - ema_elder) / cp + df['elder_bear'] = (l - ema_elder) / cp + df['elder_dist'] = (c - ema_elder) / cp + + # --- EMA（固定若干周期作辅助）--- + for p in [5, 8, 13, 21, 50, 120]: + df[f'ema_{p}'] = c.ewm(span=p, adjust=False).mean() + df['ema_fast_slow'] = (df['ema_8'] - df['ema_21']) / cp + df['price_vs_ema120'] = (c - df['ema_120']) / cp + df['ema8_slope'] = df['ema_8'].pct_change(5) + + # --- MACD --- + ema12 = c.ewm(span=12, adjust=False).mean() + ema26 = c.ewm(span=26, adjust=False).mean() + df['macd'] = (ema12 - ema26) / cp + df['macd_signal'] = df['macd'].ewm(span=9, adjust=False).mean() + df['macd_hist'] = df['macd'] - df['macd_signal'] + + # --- ATR 比例 --- + df['atr_14'] = tr.rolling(14).mean() + df['atr_pct'] = df['atr_14'] / cp + + # --- 动量与波动 --- + for p in [1, 3, 5, 10, 20]: + df[f'ret_{p}'] = c.pct_change(p) + df['vol_5'] = c.pct_change().rolling(5).std() + df['vol_20'] = c.pct_change().rolling(20).std() + + # --- K线形态与时间 --- + body = (c - o).abs() + df['body_pct'] = body / cp + df['price_position_20'] = (c - l.rolling(20).min()) / (h.rolling(20).max() - l.rolling(20).min()).replace(0, np.nan) + df['hour_sin'] = np.sin(2 * np.pi * df.index.hour / 24) + df['hour_cos'] = np.cos(2 * np.pi * df.index.hour / 24) + + return df + + +def get_feature_cols(df: pd.DataFrame): + """排除原始价格与各通道上下轨/中轨原始列，只保留衍生特征""" + exclude = { + 'ts', 'open', 'high', 'low', 'close', 'label', + 'bb_upper', 'bb_lower', 'bb_mid', 'atr_14', + 'keltner_upper', 'keltner_lower', 'keltner_mid', + 'donchian_upper', 'donchian_lower', 'donchian_mid', + 'atr_band_upper', 'atr_band_lower', 'atr_band_mid', + 'lr_upper', 'lr_lower', 'lr_mid', + 'median_band_upper', 'median_band_lower', 'median_band_mid', + 'chandelier_upper', 'chandelier_lower', 'chandelier_mid', + 'std_band_upper', 'std_band_lower', 'std_band_mid', + 'ema_5', 'ema_8', 'ema_13', 'ema_21', 'ema_50', 'ema_120', + } + return [c for c in df.columns if c not in exclude + and df[c].dtype in ('float64', 'float32', 'int64', 'int32')] + + +# ==================== 标签 ==================== +def add_labels(df: pd.DataFrame, forward_bars: int = 10, threshold: float = 0.002) -> pd.DataFrame: + """未来 N 根收益率 > threshold → 1(多), < -threshold → -1(空), 否则 0""" + future_ret = df['close'].shift(-forward_bars) / df['close'] - 1 + df = df.copy() + df['label'] = 0 + df.loc[future_ret > threshold, 'label'] = 1 + df.loc[future_ret < -threshold, 'label'] = -1 + return df + + +# ==================== 滚动训练 ==================== +def train_predict_walkforward( + df: pd.DataFrame, + feature_cols: list, + train_months: int = 3, + lgb_rounds: int = 250, +): + """滚动：用过去 train_months 月训练，预测下一个月""" + df = df.copy() + df['month'] = df.index.to_period('M') + months = sorted(df['month'].unique()) + all_proba_long = pd.Series(0.0, index=df.index) + all_proba_short = pd.Series(0.0, index=df.index) + last_model = None + + for i in range(train_months, len(months)): + test_month = months[i] + train_start = months[i - train_months] + train_mask = (df['month'] >= train_start) & (df['month'] < test_month) + test_mask = df['month'] == test_month + train_df = df[train_mask].dropna(subset=feature_cols + ['label']) + test_df = df[test_mask].dropna(subset=feature_cols) + if len(train_df) < 1000 or len(test_df) < 100: + continue + X_train = train_df[feature_cols].values + y_train = (train_df['label'].values + 1).astype(int) # -1,0,1 -> 0,1,2 + X_test = test_df[feature_cols].values + params = { + 'objective': 'multiclass', + 'num_class': 3, + 'metric': 'multi_logloss', + 'learning_rate': 0.05, + 'num_leaves': 31, + 'max_depth': 6, + 'min_child_samples': 50, + 'subsample': 0.8, + 'colsample_bytree': 0.8, + 'reg_alpha': 0.1, + 'reg_lambda': 0.1, + 'verbose': -1, + 'n_jobs': -1, + 'seed': 42, + } + dtrain = lgb.Dataset(X_train, label=y_train) + model = lgb.train(params, dtrain, num_boost_round=lgb_rounds) + last_model = model + proba = model.predict(X_test) # (n, 3) -> [P(short), P(neutral), P(long)] + test_idx = test_df.index + all_proba_short.loc[test_idx] = proba[:, 0] + all_proba_long.loc[test_idx] = proba[:, 2] + + return all_proba_long, all_proba_short, last_model + + +def train_on_period_predict_on_other( + df_train: pd.DataFrame, + df_test: pd.DataFrame, + feature_cols: list, + forward_bars: int, + label_threshold: float, + lgb_rounds: int = 250, +): + """ + 在 df_train 上训练一个模型，在 df_test 上预测（严格样本外）。 + df_train 需含 label 列；df_test 只需含 feature_cols。 + 返回 (proba_long_series, proba_short_series, model) + """ + train_df = df_train.dropna(subset=feature_cols + ['label']) + if len(train_df) < 2000: + return None, None, None + X_train = train_df[feature_cols].values + y_train = (train_df['label'].values + 1).astype(int) + test_df = df_test.dropna(subset=feature_cols) + if len(test_df) < 100: + return None, None, None + X_test = test_df[feature_cols].values + params = { + 'objective': 'multiclass', + 'num_class': 3, + 'metric': 'multi_logloss', + 'learning_rate': 0.05, + 'num_leaves': 31, + 'max_depth': 6, + 'min_child_samples': 50, + 'subsample': 0.8, + 'colsample_bytree': 0.8, + 'reg_alpha': 0.1, + 'reg_lambda': 0.1, + 'verbose': -1, + 'n_jobs': -1, + 'seed': 42, + } + dtrain = lgb.Dataset(X_train, label=y_train) + model = lgb.train(params, dtrain, num_boost_round=lgb_rounds) + proba = model.predict(X_test) + all_proba_long = pd.Series(0.0, index=df_test.index) + all_proba_short = pd.Series(0.0, index=df_test.index) + all_proba_short.loc[test_df.index] = proba[:, 0] + all_proba_long.loc[test_df.index] = proba[:, 2] + return all_proba_long, all_proba_short, model + + +# ==================== 回测（单仓位、100U×100倍） ==================== +def backtest( + df: pd.DataFrame, + proba_long: pd.Series, + proba_short: pd.Series, + notional: float = NOTIONAL_PER_TRADE, + prob_threshold: float = 0.45, + min_hold_seconds: int = 180, + max_hold_seconds: int = 1800, + sl_pct: float = 0.004, + tp_pct: float = 0.006, +) -> list: + """同一时间仅 1 仓，开仓即 10000U 名义（100U×100倍）""" + pos = 0 + open_price = 0.0 + open_time = None + trades = [] + + for i in range(len(df)): + dt = df.index[i] + price = df['close'].iloc[i] + pl = proba_long.iloc[i] + ps = proba_short.iloc[i] + + if pos != 0 and open_time is not None: + pnl_pct = (price - open_price) / open_price if pos == 1 else (open_price - price) / open_price + hold_sec = (dt - open_time).total_seconds() + + # 硬止损 + if -pnl_pct >= sl_pct * 1.5: + pnl_usdt = notional * pnl_pct + trades.append((pos, open_price, price, pnl_usdt, hold_sec, '硬止损', open_time, dt)) + pos = 0 + continue + if hold_sec >= min_hold_seconds: + if -pnl_pct >= sl_pct: + pnl_usdt = notional * pnl_pct + trades.append((pos, open_price, price, pnl_usdt, hold_sec, '止损', open_time, dt)) + pos = 0 + continue + if pnl_pct >= tp_pct: + pnl_usdt = notional * pnl_pct + trades.append((pos, open_price, price, pnl_usdt, hold_sec, '止盈', open_time, dt)) + pos = 0 + continue + if hold_sec >= max_hold_seconds: + pnl_usdt = notional * pnl_pct + trades.append((pos, open_price, price, pnl_usdt, hold_sec, '超时', open_time, dt)) + pos = 0 + continue + # AI 反向信号平仓 + if pos == 1 and ps > prob_threshold + 0.05: + pnl_usdt = notional * pnl_pct + trades.append((pos, open_price, price, pnl_usdt, hold_sec, 'AI反转', open_time, dt)) + pos = 0 + elif pos == -1 and pl > prob_threshold + 0.05: + pnl_usdt = notional * pnl_pct + trades.append((pos, open_price, price, pnl_usdt, hold_sec, 'AI反转', open_time, dt)) + pos = 0 + + if pos == 0: + if pl > prob_threshold and pl > ps: + pos = 1 + open_price = price + open_time = dt + elif ps > prob_threshold and ps > pl: + pos = -1 + open_price = price + open_time = dt + + if pos != 0: + price = df['close'].iloc[-1] + dt = df.index[-1] + pnl_pct = (price - open_price) / open_price if pos == 1 else (open_price - price) / open_price + hold_sec = (dt - open_time).total_seconds() + trades.append((pos, open_price, price, notional * pnl_pct, hold_sec, '结束', open_time, dt)) + + return trades + + +# ==================== 结果分析 ==================== +def analyze_trades(trades: list, notional: float = NOTIONAL_PER_TRADE, initial_balance: float = INITIAL_BALANCE) -> dict: + """ + 统计净利、胜率、回撤、月均净利等。 + 回撤按「可用资金」计算：手续费当日扣全额，返佣次日 8 点才到账，故 dd_available 更保守。 + """ + if not trades: + return {'n': 0, 'net': 0.0, 'wr': 0.0, 'dd': 0.0, 'dd_available': 0.0, 'total_pnl': 0.0, 'monthly_net': 0.0, 'months': 0} + n = len(trades) + total_pnl = sum(t[3] for t in trades) + net = total_pnl - NET_FEE_PER_TRADE * n + wins = len([t for t in trades if t[3] > 0]) + wr = wins / n * 100 + # 简单回撤（净手续费已扣，不含返佣延迟） + cum = 0.0 + peak = 0.0 + dd = 0.0 + for t in trades: + cum += t[3] - NET_FEE_PER_TRADE + if cum > peak: + peak = cum + if peak - cum > dd: + dd = peak - cum + # 可用资金回撤：平仓时扣全额手续费，返佣次日 8 点才到账（简化为 +1 天） + events = [] # (datetime, delta_balance) + for t in trades: + close_time = t[7] + events.append((close_time, t[3] - FULL_FEE_PER_TRADE)) + try: + next_day = close_time + datetime.timedelta(days=1) + except Exception: + next_day = close_time + events.append((next_day, REBATE_PER_TRADE)) + events.sort(key=lambda x: x[0]) + balance = initial_balance + peak_bal = balance + dd_available = 0.0 + for _, delta in events: + balance += delta + if balance > peak_bal: + peak_bal = balance + if peak_bal - balance > dd_available: + dd_available = peak_bal - balance + # 按平仓时间所在月汇总净利 + monthly_net = defaultdict(float) + for t in trades: + close_time = t[7] + month_key = close_time.strftime('%Y-%m') if hasattr(close_time, 'strftime') else str(close_time)[:7] + monthly_net[month_key] += t[3] - NET_FEE_PER_TRADE + num_months = len(monthly_net) or 1 + avg_monthly_net = sum(monthly_net.values()) / num_months + return { + 'n': n, + 'net': net, + 'wr': wr, + 'dd': dd, + 'dd_available': dd_available, + 'total_pnl': total_pnl, + 'avg_pnl': net / n, + 'monthly_net': avg_monthly_net, + 'months': num_months, + 'monthly_detail': dict(monthly_net), + } + + +TARGET_MONTHLY_NET = 1000.0 # 目标月均净利 (USDT) + +def print_report(trades: list, label: str = ""): + """打印回测报告：本金、可用资金最大回撤(返佣次日到账)、月均净利及双目标""" + if not trades: + print(f" [{label}] 无交易", flush=True) + return + r = analyze_trades(trades) + reasons = defaultdict(int) + for t in trades: + reasons[t[5]] += 1 + dd_ok = r['dd_available'] <= MAX_DD_TARGET + monthly_ok = r['monthly_net'] >= TARGET_MONTHLY_NET + print(f"\n === {label} ===", flush=True) + print(f" 本金: {INITIAL_BALANCE:.0f} USDT | 交易: {r['n']} 笔 | 总净利: {r['net']:+.2f} USDT | 胜率: {r['wr']:.1f}%", flush=True) + print(f" 可用资金最大回撤: {r['dd_available']:.2f} USDT (返佣次日到账) | 目标 ≤{MAX_DD_TARGET:.0f}U: {'达标' if dd_ok else '未达标'}", flush=True) + print(f" 月均净利: {r['monthly_net']:+.2f} USDT (共 {r['months']} 个月) | 目标 ≥{TARGET_MONTHLY_NET:.0f}U/月: {'达标' if monthly_ok else '未达标'}", flush=True) + print(f" 平仓原因: {dict(reasons)}", flush=True) + + +# ==================== 模型保存 ==================== +def save_model_and_params(model, feature_cols: list, params: dict, path: Path = None): + """保存 LightGBM 模型和策略参数""" + if path is None: + path = Path(__file__).parent / 'models' / 'eth_ai_strategy' + path = Path(path) + path.mkdir(parents=True, exist_ok=True) + model.save_model(str(path / 'model.txt')) + with open(path / 'feature_cols.json', 'w', encoding='utf-8') as f: + json.dump(feature_cols, f, ensure_ascii=False, indent=2) + with open(path / 'strategy_params.json', 'w', encoding='utf-8') as f: + json.dump(params, f, ensure_ascii=False, indent=2) + print(f" 模型与参数已保存至: {path}", flush=True) + + +# ==================== 主流程：2020 训练 + 2021 真实回测 ==================== +def run_single( + df_train: pd.DataFrame, + df_test: pd.DataFrame, + ind_params: dict, + forward_bars: int, + label_threshold: float, + prob_threshold: float, + sl_pct: float, + tp_pct: float, + min_hold: int, + max_hold: int, +): + """ + 用 df_train（2020）训练模型，在 df_test（2021）上预测并回测，严格样本外。 + 返回 (trades_2021, model, feature_cols)。 + """ + df_tr = add_features(df_train.copy(), ind=ind_params) + df_te = add_features(df_test.copy(), ind=ind_params) + feature_cols = get_feature_cols(df_tr) + df_tr = add_labels(df_tr, forward_bars=forward_bars, threshold=label_threshold) + proba_long, proba_short, model = train_on_period_predict_on_other( + df_tr, df_te, feature_cols, forward_bars, label_threshold, + ) + if model is None: + return [], None, None + trades = backtest( + df_te, proba_long, proba_short, + notional=NOTIONAL_PER_TRADE, + prob_threshold=prob_threshold, + min_hold_seconds=min_hold, + max_hold_seconds=max_hold, + sl_pct=sl_pct, + tp_pct=tp_pct, + ) + return trades, model, feature_cols + + +def _grid_indicator_and_strategy(max_configs: int = 220, seed: int = 42): + """随机采样「各类均值回归/带型指标参数 + 策略参数」组合，各种组合、各种指标参数一起调""" + rng = np.random.default_rng(seed) + ind_base = default_indicator_params() + # 布林类 + bb_opts = [(20, 2.0), (20, 2.5), (30, 2.0), (15, 2.0), (14, 2.0)] + keltner_opts = [(20, 2.0), (20, 2.5), (14, 2.0), (22, 2.5)] + donchian_opts = [14, 20, 30] + stoch_opts = [(14, 3), (10, 3), (20, 5)] + cci_opts = [14, 20] + rsi_opts = [7, 14, 21] + atr_band_opts = [(20, 2.0), (14, 2.0), (22, 2.5)] + # 新增均值回归类 + zscore_opts = [14, 20, 30] + lr_opts = [(20, 2.0), (14, 2.0), (30, 2.5)] + median_band_opts = [14, 20, 30] + pct_rank_opts = [14, 20, 30] + chandelier_opts = [(22, 3.0), (14, 2.5), (30, 3.5)] + std_band_opts = [(14, 2.0), (20, 2.0), (14, 2.5)] + elder_opts = [10, 13, 20] + # 策略：加入更保守选项以压低回撤（紧止损、高置信） + label_opts = [(8, 0.0015), (10, 0.002), (10, 0.003), (15, 0.002), (20, 0.003)] + sl_tp_opts = [ + (0.002, 0.004), (0.0025, 0.005), (0.003, 0.005), (0.003, 0.006), + (0.004, 0.006), (0.004, 0.008), (0.005, 0.008), (0.005, 0.010), + ] + prob_opts = [0.42, 0.45, 0.48, 0.50] + out = [] + for _ in range(max_configs): + bb_p, bb_s = bb_opts[rng.integers(0, len(bb_opts))] + kp, ka = keltner_opts[rng.integers(0, len(keltner_opts))] + dc = donchian_opts[rng.integers(0, len(donchian_opts))] + sk, sd = stoch_opts[rng.integers(0, len(stoch_opts))] + cci_p = cci_opts[rng.integers(0, len(cci_opts))] + rsi_p = rsi_opts[rng.integers(0, len(rsi_opts))] + abp, abm = atr_band_opts[rng.integers(0, len(atr_band_opts))] + zp = zscore_opts[rng.integers(0, len(zscore_opts))] + lrp, lrm = lr_opts[rng.integers(0, len(lr_opts))] + mdp = median_band_opts[rng.integers(0, len(median_band_opts))] + prp = pct_rank_opts[rng.integers(0, len(pct_rank_opts))] + cep, cem = chandelier_opts[rng.integers(0, len(chandelier_opts))] + sbp, sbm = std_band_opts[rng.integers(0, len(std_band_opts))] + ep = elder_opts[rng.integers(0, len(elder_opts))] + fb, th = label_opts[rng.integers(0, len(label_opts))] + sl, tp = sl_tp_opts[rng.integers(0, len(sl_tp_opts))] + prob = prob_opts[rng.integers(0, len(prob_opts))] + ind = dict(ind_base, + bb_period=bb_p, bb_std=bb_s, + keltner_period=kp, keltner_atr_mult=ka, + donchian_period=dc, + stoch_k_period=sk, stoch_d_period=sd, + cci_period=cci_p, rsi_period=rsi_p, + atr_band_period=abp, atr_band_mult=abm, + zscore_period=zp, + lr_period=lrp, lr_std_mult=lrm, + median_band_period=mdp, + pct_rank_period=prp, + chandelier_period=cep, chandelier_mult=cem, + std_band_period=sbp, std_band_mult=sbm, + elder_period=ep, + ) + out.append({ + 'ind': ind, + 'forward_bars': fb, + 'label_threshold': th, + 'sl_pct': sl, + 'tp_pct': tp, + 'prob_threshold': prob, + 'min_hold': 180, + 'max_hold': 1800, + }) + return out + + +def run_cycle_compare(periods: list, do_save_best: bool = True): + """ + 多周期对比：对每个周期用同一套默认参数做 2020 训练 + 2021 回测，比较 2021 年月均净利等。 + periods: 如 [5, 15] 表示 5m、15m；或 [1, 3, 5, 15, 30, 60] 表示全部周期。 + """ + t0 = _time.time() + period_labels = [_normalize_period(p) for p in periods] + print("=" * 60, flush=True) + print(" ETH 合约 — 多周期回测对比 (2020 训练 / 2021 回测)", flush=True) + print(f" 参与周期: {', '.join(period_labels)}", flush=True) + print("=" * 60, flush=True) + ind = default_indicator_params() + results = [] + best_net = -1e9 + best_row = None + for i, period in enumerate(period_labels): + print(f"\n [{i+1}/{len(period_labels)}] 周期 {period} ...", flush=True) + try: + df_2020 = load_klines(TRAIN_START, TRAIN_END_EXCLUSIVE, period=period) + df_2021 = load_klines(TEST_START, TEST_END_EXCLUSIVE, period=period) + except Exception as e: + print(f" 跳过 {period}: {e}", flush=True) + results.append({'period': period, 'ok': False, 'error': str(e)}) + continue + print(f" 数据: 2020 {len(df_2020):,} 根 | 2021 {len(df_2021):,} 根", flush=True) + trades, model, feature_cols = run_single( + df_2020, df_2021, ind, + forward_bars=10, label_threshold=0.002, + prob_threshold=0.45, sl_pct=0.004, tp_pct=0.006, + min_hold=180, max_hold=1800, + ) + if not trades or model is None: + print(f" {period}: 无有效交易", flush=True) + results.append({'period': period, 'ok': False}) + continue + r = analyze_trades(trades) + results.append({ + 'period': period, + 'ok': True, + 'n': r['n'], + 'net': r['net'], + 'monthly_net': r['monthly_net'], + 'wr': r['wr'], + 'dd': r['dd'], + 'dd_available': r['dd_available'], + 'trades': trades, + 'model': model, + 'feature_cols': feature_cols, + }) + print(f" {period}: 交易 {r['n']} 笔 | 月均 {r['monthly_net']:+.2f} USDT | 可用回撤 {r['dd_available']:.2f} USDT | 胜率 {r['wr']:.1f}%", flush=True) + if r['monthly_net'] > best_net: + best_net = r['monthly_net'] + best_row = results[-1] + # 打印对比表 + ok_results = [x for x in results if x.get('ok')] + print("\n" + "=" * 60, flush=True) + print(" 多周期对比结果 (2021 年样本外)", flush=True) + print("=" * 60, flush=True) + if not ok_results: + print(" 无有效回测结果。", flush=True) + return + print(f" {'周期':<6} {'交易数':>8} {'总净利':>12} {'月均净利':>12} {'可用回撤':>10} {'胜率':>8}", flush=True) + print(" " + "-" * 62, flush=True) + for x in ok_results: + print(f" {x['period']:<6} {x['n']:>8} {x['net']:>+12.2f} {x['monthly_net']:>+12.2f} {x.get('dd_available', x['dd']):>10.2f} {x['wr']:>7.1f}%", flush=True) + # 优选：回撤≤500 且 月盈≥1000；否则月均最高 + def _cmp_cycle(a): + dd_ok = a.get('dd_available', a['dd']) <= MAX_DD_TARGET + mo_ok = a['monthly_net'] >= TARGET_MONTHLY_NET + return (dd_ok, mo_ok, a['monthly_net'], -a.get('dd_available', a['dd'])) + best = max(ok_results, key=_cmp_cycle) + best_row = best + print(" " + "-" * 62, flush=True) + b_dd = best.get('dd_available', best['dd']) + print(f" 最佳周期: {best['period']} (月均 {best['monthly_net']:+.2f} USDT, 可用回撤 {b_dd:.2f} USDT)", flush=True) + if b_dd <= MAX_DD_TARGET and best['monthly_net'] >= TARGET_MONTHLY_NET: + print(f" 双目标达标: 回撤≤{MAX_DD_TARGET:.0f}U 且 月盈≥{TARGET_MONTHLY_NET:.0f}U", flush=True) + if do_save_best and best_row and best_row.get('model') is not None: + flat_params = { + **ind, + 'forward_bars': 10, 'label_threshold': 0.002, + 'prob_threshold': 0.45, 'sl_pct': 0.004, 'tp_pct': 0.006, + 'min_hold_seconds': 180, 'max_hold_seconds': 1800, + 'margin_per_trade': MARGIN_PER_TRADE, 'leverage': LEVERAGE, + 'notional': NOTIONAL_PER_TRADE, 'rebate_rate': REBATE_RATE, + 'train_period': f'{TRAIN_START} ~ {TRAIN_END_EXCLUSIVE}', + 'test_period': f'{TEST_START} ~ {TEST_END_EXCLUSIVE}', + 'kline_period': best['period'], + 'initial_balance': INITIAL_BALANCE, + 'max_dd_target': MAX_DD_TARGET, + } + save_model_and_params(best_row['model'], best_row.get('feature_cols', []), flat_params) + print(f" 已保存最佳周期 {best['period']} 的模型与参数。", flush=True) + print(f"\n 总耗时: {_time.time() - t0:.1f}s", flush=True) + print("=" * 60, flush=True) + + +def main(do_grid_search: bool = True, period: str = '1m'): + """ + 2020 年全年训练，2021 年全年真实回测（严格样本外）。 + period: K 线周期 '1m'|'5m'|'15m' 等。 + 参数搜索时在 2021 年月均净利上选最佳并保存。 + """ + period = _normalize_period(period) + t0 = _time.time() + print("=" * 60, flush=True) + print(f" ETH 合约 AI 策略 — 2020 训练 / 2021 真实回测 | K线周期 {period} | 单仓 100U×100倍 | 90% 返佣", flush=True) + print(f" 本金: {INITIAL_BALANCE:.0f} USDT | 返佣次日 8 点到账，回撤按可用资金计算", flush=True) + print(f" 目标: 可用资金最大回撤 ≤ {MAX_DD_TARGET:.0f} USDT，月均净利 ≥ {TARGET_MONTHLY_NET:.0f} USDT", flush=True) + print("=" * 60, flush=True) + + print(f"\n[1/4] 加载 K 线 (周期 {period}, 训练 2020 / 回测 2021)...", flush=True) + df_2020 = load_klines(TRAIN_START, TRAIN_END_EXCLUSIVE, period=period) + df_2021 = load_klines(TEST_START, TEST_END_EXCLUSIVE, period=period) + print(f" 2020 训练: {len(df_2020):,} 根 | 2021 回测: {len(df_2021):,} 根", flush=True) + if len(df_2020) < 10000: + print(" 警告: 2020 年数据不足 10000 根，请先运行 抓取多周期K线.py 拉取 2020 年数据。", flush=True) + if len(df_2021) < 1000: + print(" 警告: 2021 年数据不足，回测结果可能不可靠。", flush=True) + + # 优选：回撤≤500 且 月盈≥1000；其次回撤≤500；再其次月盈≥1000；否则取综合最优 + def _score(r): + dd_ok = r['dd_available'] <= MAX_DD_TARGET + monthly_ok = r['monthly_net'] >= TARGET_MONTHLY_NET + return (dd_ok, monthly_ok, r['monthly_net'], -r['dd_available']) + + best_score = (-1, -1, -1e9, 1e9) + best_result = None + + if do_grid_search: + configs = list(_grid_indicator_and_strategy()) + print(f"\n[2/4] 参数搜索 (共 {len(configs)} 组): 目标 回撤≤{MAX_DD_TARGET:.0f}U 且 月盈≥{TARGET_MONTHLY_NET:.0f}U ...", flush=True) + for i, cfg in enumerate(configs): + _t_start = _time.time() + trades, model, feature_cols = run_single( + df_2020, + df_2021, + cfg['ind'], + cfg['forward_bars'], + cfg['label_threshold'], + cfg['prob_threshold'], + cfg['sl_pct'], + cfg['tp_pct'], + cfg['min_hold'], + cfg['max_hold'], + ) + _elapsed = _time.time() - _t_start + if not trades or model is None: + if (i + 1) % 10 == 0 or i == 0: + print(f" 组 {i+1}/{len(configs)} 完成 (本组 {_elapsed:.0f}s，无有效交易)", flush=True) + continue + r = analyze_trades(trades) + score = _score(r) + if score > best_score: + best_score = score + best_result = (trades, model, feature_cols, { + 'indicator_params': cfg['ind'], + 'forward_bars': cfg['forward_bars'], + 'label_threshold': cfg['label_threshold'], + 'prob_threshold': cfg['prob_threshold'], + 'sl_pct': cfg['sl_pct'], + 'tp_pct': cfg['tp_pct'], + 'min_hold_seconds': cfg['min_hold'], + 'max_hold_seconds': cfg['max_hold'], + 'margin_per_trade': MARGIN_PER_TRADE, + 'leverage': LEVERAGE, + 'notional': NOTIONAL_PER_TRADE, + 'rebate_rate': REBATE_RATE, + 'train_period': f'{TRAIN_START} ~ {TRAIN_END_EXCLUSIVE}', + 'test_period': f'{TEST_START} ~ {TEST_END_EXCLUSIVE}', + 'kline_period': period, + 'initial_balance': INITIAL_BALANCE, + 'max_dd_target': MAX_DD_TARGET, + }) + _total_so_far = _time.time() - t0 + _avg_per = _total_so_far / (i + 1) + _left = _avg_per * (len(configs) - i - 1) + if (i + 1) % 10 == 0 or i == 0: + print(f" 组 {i+1}/{len(configs)} 完成 | 本组 {_elapsed:.0f}s | 回撤 {r['dd_available']:.0f} 月均 {r['monthly_net']:+.0f} | 预计剩余 ~{_left/60:.0f}min", flush=True) + else: + print("\n[2/4] 使用默认参数: 2020 训练 -> 2021 回测...", flush=True) + ind = default_indicator_params() + trades, model, feature_cols = run_single( + df_2020, df_2021, ind, + forward_bars=10, label_threshold=0.002, + prob_threshold=0.45, sl_pct=0.004, tp_pct=0.006, + min_hold=180, max_hold=1800, + ) + if trades and model is not None: + r = analyze_trades(trades) + best_monthly = r['monthly_net'] + reached_target = r['monthly_net'] >= TARGET_MONTHLY_NET + best_result = (trades, model, feature_cols, { + 'indicator_params': ind, + 'forward_bars': 10, + 'label_threshold': 0.002, + 'prob_threshold': 0.45, + 'sl_pct': 0.004, + 'tp_pct': 0.006, + 'min_hold_seconds': 180, + 'max_hold_seconds': 1800, + 'margin_per_trade': MARGIN_PER_TRADE, + 'leverage': LEVERAGE, + 'notional': NOTIONAL_PER_TRADE, + 'rebate_rate': REBATE_RATE, + 'train_period': f'{TRAIN_START} ~ {TRAIN_END_EXCLUSIVE}', + 'test_period': f'{TEST_START} ~ {TEST_END_EXCLUSIVE}', + 'kline_period': period, + 'initial_balance': INITIAL_BALANCE, + 'max_dd_target': MAX_DD_TARGET, + }) + + if best_result is None: + print(" 未得到有效模型/交易（可能 2020 或 2021 数据不足）。", flush=True) + return + trades, model, feature_cols, full_params = best_result + print("\n[3/4] 2021 年真实回测结果 (最佳参数)...", flush=True) + print_report(trades, "2021 年样本外回测") + flat_params = {**full_params.get('indicator_params', {}), **{k: v for k, v in full_params.items() if k != 'indicator_params'}} + save_model_and_params(model, feature_cols, flat_params) + r = analyze_trades(trades) + elapsed = _time.time() - t0 + dd_ok = r['dd_available'] <= MAX_DD_TARGET + monthly_ok = r['monthly_net'] >= TARGET_MONTHLY_NET + print(f"\n[4/4] 总耗时: {elapsed:.1f}s | 可用资金回撤: {r['dd_available']:.2f} USDT (目标≤{MAX_DD_TARGET:.0f}): {'达标' if dd_ok else '未达标'} | 月均净利: {r['monthly_net']:+.2f} USDT (目标≥{TARGET_MONTHLY_NET:.0f}): {'达标' if monthly_ok else '未达标'}", flush=True) + print("=" * 60, flush=True) + + +if __name__ == '__main__': + import sys + argv = sys.argv + # 多周期对比：默认只比 5m 和 15m；加 --all 则比 1m,3m,5m,15m,30m,1h + if '--compare' in argv or '-c' in argv: + periods = [5, 15] if '--all' not in argv else [1, 3, 5, 15, 30, 60] + run_cycle_compare(periods, do_save_best=True) + else: + # 单周期：--period 5m 或 --period 15 指定周期，否则默认 1m + period = '1m' + for i, a in enumerate(argv): + if a in ('--period', '-p') and i + 1 < len(argv): + period = argv[i + 1] + break + do_search = '--no-search' not in argv + main(do_grid_search=do_search, period=period)