initial vault sync

00 附件/1. 软件行业工程信息安全的隐患、原因及解决办法.md

@@ -0,0 +1,23 @@
+工程信息管理安全问题
+
+### 1. 软件行业工程信息安全的隐患、原因及解决办法
+
+以下结合实际案例，针对开发到运维各环节的典型隐患展开说明：
+
+| 隐患类型       | 具体案例                                                     | 存在原因                                                     | 解决办法                                                     |
+| -------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
+| 代码注入漏洞   | 登录时简单拼接SQL方式验证用户名密码，则输入特殊语句绕过验证可直接登录系统 | 开发者编码规范意识不足，为图便捷简化代码逻辑，未做输入过滤   | 采用参数化查询，用SonarQube等工具做静态代码扫描，拦截高危代码 |
+| 第三方组件漏洞 | [Cloudflare严重宕机事故，导致全球半个互联网瘫痪！](https://blog.csdn.net/qq_41603102/article/details/155240625) | 疏于管理第三方依赖，未定期排查组件安全状态                   | 用OWASP Dependency - Check定期扫描依赖包，及时升级高危漏洞版本组件 |
+| 权限管控不严   | 用户看到超过自己权限的数据                                   | 测试仅关注功能正常场景，未开展越权测试，权限校验逻辑设计简陋 | 实施灰盒测试，覆盖水平越权和垂直越权场景，按最小权限原则分配账户权限 |
+| 配置失误泄露   | 误将账号密码等配置文件放Web根目录，被搜索引擎抓取            | 运维人员安全意识薄弱，缺乏统一的部署配置规范                 | 建立配置基线，删除默认账号、禁用不必要的HTTP方法，禁用生产环境DEBUG模式 |
+| 数据泄露       | 客户测试数据未加密且通过微信、邮箱等传输                     | 忽视信息安全问题，未建立数据管理机制                         | 对客户数据进行统一管理，使用内网环境传输数据且进行加密       |
+
+### 2. 软件工程全生命周期信息管理各环节常见问题
+
+软件工程全生命周期通常涵盖需求分析、设计、编码、测试、部署运维等环节，各环节信息管理常见问题如下：
+
+1. **需求分析阶段**：需求文档模糊，未明确异常场景和边界条件，比如支付接口未定义重复支付的处理规则，导致用户重复点击多次扣款；同时易出现需求传递偏差，开发团队与业务方沟通不足，致使理解的需求与实际需求不符。
+2. **设计阶段**：架构设计不合理，如未考虑高并发场景引发系统卡顿，或未设计数据备份机制存在单点故障风险；接口设计缺乏统一标准，后续各模块对接时出现信息交互混乱，且设计文档未同步更新，导致后续开发无准确参考。
+3. **编码阶段**：代码规范性差，存在空指针异常、数组越界等问题，如用SimpleDateFormat引发线程安全问题导致时间解析错误；此外依赖管理混乱，第三方接口调用未设置超时重试机制，且版本冲突易引发类加载异常。
+4. **测试阶段**：测试覆盖不全面，未覆盖网络延迟、大流量等极端场景；测试环境与生产环境配置差异大，如数据库字符集不同，导致测试通过的功能上线后出现异常；同时测试用例和测试报告等信息记录不全，故障追溯困难。
+5. **部署运维阶段**：部署文档缺失或错误，导致部署出现异常；配置信息混乱，如生产环境误用测试环境配置；监控告警机制不完善，未监控Redis内存使用率等核心指标，缓存击穿引发数据库雪崩；且日志管理不当，DEBUG日志导致磁盘爆满，或日志缺失难以排查故障。

00 附件/My_strategy.py

@@ -0,0 +1,165 @@
+
+import time
+import pandas as pd
+import ccxt
+from datetime import timedelta
+# import pandas_ta
+import talib
+
+# ====================================================================================================
+# =====格式设置
+# ====================================================================================================
+pd.set_option('display.max_rows', 1000)
+pd.set_option('expand_frame_repr', False)  # 当列太多时不换行
+# 设置命令行输出时的列对齐功能
+pd.set_option('display.unicode.ambiguous_as_wide', True)
+pd.set_option('display.unicode.east_asian_width', True)
+
+# ====================================================================================================
+# =====创建ccxt交易所
+# ====================================================================================================
+BINANCE_CONFIG = {
+                    'apiKey': '',
+                    'secret': '',
+                    'proxies': {'http': '127.0.0.1:7890', 'https': '127.0.0.1:7890'}
+                  }
+exchange = ccxt.binance(BINANCE_CONFIG)
+
+
+
+while True:
+    # list = ['DOGEUSDT', 'ETHUSDT', 'BTCUSDT']
+    #
+    # # for symbol in list:
+    symbol = 'ETHUSDT'
+    time_interval = '1m'  # 其他可以尝试的值：'1m', '5m', '15m', '30m', '1h', '2h', '1d', '1w', '1M', '1y'，并不是每个交易所都支持
+    bar_num = 1000  # 获取K线的数量
+    params = {'symbol': symbol,  # 交易币对
+              'interval': time_interval,  # 时间间隔
+              'limit': bar_num}  # 数据条数
+
+    # ====================================================================================================
+    # =====获取K线数据
+    # ====================================================================================================
+    response = exchange.fapiPublicGetKlines(params=params)
+    k_lines = pd.DataFrame(response)
+    # print(k_lines)
+
+    # =====整理K线数据
+    df = pd.DataFrame(response, dtype=float)  # 将数据转换为dataframe
+    df.rename(columns={0: 'MTS', 1: 'Open', 2: 'High',
+                       3: 'Low', 4: 'Close', 5: 'Volume'}, inplace=True)  # 重命名
+    df['candle_begin_time'] = pd.to_datetime(df['MTS'], unit='ms')  # 整理时间
+    df['candle_begin_time_GMT8'] = df['candle_begin_time'] + timedelta(hours=8)  # 北京时间
+    df = df[['candle_begin_time_GMT8', 'Open', 'High', 'Low', 'Close', 'Volume']]  # 整理列的顺序
+
+    # ====================================================================================================
+    # =====获取币对的最新价格
+    # ====================================================================================================
+    data = exchange.fapiPublicGetTickerPrice(params={'symbol': "ETHUSDT"})
+    price_ETH = data['price']
+
+    # ====================================================================================================
+    # =====通过pandas-ta计算指标并加入相关指标计算列
+    # ====================================================================================================
+    ## 计算均线
+    # df.ta.sma(length=5, append=True, col_names="SMA_5") # pandas-ta 计算SM5指标
+    df['MA5'] = talib.MA(df['Close'], timeperiod=5)  # ta-lib计算MA5指标
+
+    # # 计算14日相对强弱指数（RSI）
+    # df.ta.rsi(length=14, append=True, col_names="RSI_14")  # pandas-ta计算RSI指标
+    df['RSI_14'] = talib.RSI(df['Close'], timeperiod=14)  # ta-lib计算RSI指标
+
+    # # 计算MACD（12/26/9周期）
+    # df.ta.macd(fast=12, slow=26, signal=9, append=True)  # 默认列名：MACD_12_26_9, MACDs_12_26_9, MACDh_12_26_9 [2,7](@ref)
+
+    # # 计算布林带（20日，2倍标准差）
+    # df.ta.bbands(length=20, std=2, append=True)
+    # pandas-ta计算布林带指标
+    # 默认列名：BBL_20_2.0, BBM_20_2.0, BBU_20_2.0
+
+    ## ta-lib 计算布林带指标
+    upper_band, middle_band, lower_band = talib.BBANDS(
+        df['Close'],
+        timeperiod=20,
+        nbdevup=2,
+        nbdevdn=2,
+        matype=0  # SMA
+    )
+    df['BB_Upper'] = upper_band
+    df['BB_Middle'] = middle_band
+    df['BB_Lower'] = lower_band
+
+    ## 计算ADX指标
+    # df.ta.adx(length=20, append=True) # pandas-ta计算adx
+    df['ADX_14'] = talib.ADX(df['High'], df['Low'], df['Close'], timeperiod=14)  # ta-lib计算ADX指标
+
+    ## 计算ATR指标
+    # df.ta.atr(length=16, append=True)     # pandas-ta计算atr，默认参数14，length也可以调整
+    df['ATR_14'] = talib.ATR(df['High'], df['Low'], df['Close'], timeperiod=14)
+
+    print(df)
+    # df.to_csv('Ta4.csv')
+
+    # ====================================================================================================
+    # =====设置下单条件并执行
+    # ====================================================================================================
+    # if float(price_ETH) > float(df['Close'].iloc[-2]):
+    #     print('价格上升')
+    # else:
+    #     print('价格下降')
+    #
+    # print(price_ETH)
+    # print(df['Close'].iloc[-2])
+    # print(df['RSI_14'].iloc[-2] > 30)
+
+    # 以上为dataframe取数格式的测试，目前使用还不太熟练
+
+    ## 调整杠杆倍率
+    # params = {'symbol': 'ETHUSDT',  # 交易币对
+    #           'leverage': 10,
+    #           'timestamp': int(time.time() * 1000)}
+    # leverage = exchange.fapiPrivatePostLeverage(params=params)
+    # print('调整开仓杠杆\n', leverage, '\n')
+
+    ## 下单操作
+    num = 0
+    max_num = 3
+    # 使用变量来控制下单次数
+
+    params = {'side': 'BUY',
+              'symbol': 'ETHUSDT',  # 交易币对
+              'type': 'LIMIT',
+              # 'price': price_ETH,
+              'price': df['Close'].iloc[-1],
+              # 下单价格,在限价单的时候
+              # 启用，将type换成LIMIT
+              # 此处用变量取代了课程中的数值。以后下单的价格可能需要复杂计算再储存在变量中，做测试。
+              'quantity': 0.01,  # 下单数量
+              'timestamp': int(time.time() * 1000),
+              'timeInForce': 'GTC'}  # https://academy.binance.com/zh/articles/understanding-the-different-order-types
+
+
+
+
+    if df['RSI_14'].iloc[-2] > 30 and df['RSI_14'].iloc[-1] < 30:
+        print('向下穿越rsi30，执行买入')
+        response = exchange.fapiPrivatePostOrder(params=params)
+        order_info = pd.DataFrame(response, index=['symbol'])
+        order_id = order_info['orderId'].iloc[0]
+        print('下单\n', order_info, '\n')
+        num += 1
+        # 此后这里还可以多个条件，组合多个指标的值
+        # RSI的使用作为一个参考
+        # 通过现有数据（开高收低量），已经可以计算出不少指标了。
+
+    else:
+        print('不符合交易条件，监控中')
+        time.sleep(30)
+
+    if num >= max_num:
+        break
+
+## 这是我在办公室电脑上敲的注释
+## 测试能否通过iCloud同步成功，在家也可已经iCloud对同一个项目进行编辑。
+

00 附件/SecretKey 1.csv

@@ -0,0 +1,2 @@
+SecretId,SecretKey
+AKIDiIemiQu9AxWqWyBAvSmriMmyv0ub5NSL,kHclhQzLVSCk5D9rLbeTBh05wa2LQJ2Z

00 附件/SecretKey.csv

@@ -0,0 +1,2 @@
+SecretId,SecretKey
+AKIDiIemiQu9AxWqWyBAvSmriMmyv0ub5NSL,kHclhQzLVSCk5D9rLbeTBh05wa2LQJ2Z

00 附件/multi_tf.py

@@ -0,0 +1,138 @@
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+import numpy as np
+import talib.abstract as ta
+from freqtrade.strategy import (IStrategy, informative)
+from pandas import DataFrame, Series
+import talib.abstract as ta
+import math
+import pandas_ta as pta
+# from finta import TA as fta
+import logging
+from logging import FATAL
+
+logger = logging.getLogger(__name__)
+
+# NOT TO BE USED FOR LIVE!!!!!!
+
+class multi_tf (IStrategy):
+
+    def version(self) -> str:
+        return "v1"
+
+    INTERFACE_VERSION = 3
+
+    # ROI table:
+    minimal_roi = {
+        "0": 0.2
+    }
+
+    # Stoploss:
+    stoploss = -0.1
+
+    # Trailing stop:
+    trailing_stop = False
+    trailing_stop_positive = 0.001
+    trailing_stop_positive_offset = 0.01
+    trailing_only_offset_is_reached = True
+
+    # Sell signal
+    use_exit_signal = True
+    exit_profit_only = False
+    exit_profit_offset = 0.01
+    ignore_roi_if_entry_signal = False
+
+    timeframe = '5m'
+
+    process_only_new_candles = True
+    startup_candle_count = 100
+
+    # This method is not required.
+    # def informative_pairs(self): ...
+
+    # Define informative upper timeframe for each pair. Decorators can be stacked on same
+    # method. Available in populate_indicators as 'rsi_30m' and 'rsi_1h'.
+    @informative('30m')
+    @informative('1h')
+    def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+        return dataframe
+
+    # Define BTC/STAKE informative pair. Available in populate_indicators and other methods as
+    # 'btc_rsi_1h'. Current stake currency should be specified as {stake} format variable
+    # instead of hard-coding actual stake currency. Available in populate_indicators and other
+    # methods as 'btc_usdt_rsi_1h' (when stake currency is USDT).
+    @informative('1h', 'BTC/{stake}')
+    def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+        return dataframe
+
+    # Define BTC/ETH informative pair. You must specify quote currency if it is different from
+    # stake currency. Available in populate_indicators and other methods as 'eth_btc_rsi_1h'.
+    @informative('1h', 'ETH/BTC')
+    def populate_indicators_eth_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+        return dataframe
+
+    # Define BTC/STAKE informative pair. A custom formatter may be specified for formatting
+    # column names. A callable `fmt(**kwargs) -> str` may be specified, to implement custom
+    # formatting. Available in populate_indicators and other methods as 'rsi_fast_upper'.
+    # Resulting column names: `BTC_rsi_fast_upper_1h`, `BTC_close_1h` ...
+    @informative('1h', 'BTC/{stake}', 'BTC_{column}_{timeframe}')
+    def populate_indicators_btc_1h_2(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe['rsi_fast_upper'] = ta.RSI(dataframe, timeperiod=4)
+        return dataframe
+
+    # Define BTC/STAKE informative pair. A custom formatter may be specified for formatting
+    # column names. A callable `fmt(**kwargs) -> str` may be specified, to implement custom
+    # formatting. Available in populate_indicators and other methods as 'btc_rsi_super_fast_1h'.
+    @informative('1h', 'BTC/{stake}', '{base}_{column}_{timeframe}')
+    def populate_indicators_btc_1h_3(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe['rsi_super_fast'] = ta.RSI(dataframe, timeperiod=2)
+        return dataframe
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # Strategy timeframe indicators for current pair.
+        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+        # Informative pairs are available in this method.
+        dataframe['rsi_less'] = dataframe['rsi'] < dataframe['rsi_1h']
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        stake = self.config['stake_currency'].lower()
+        dataframe.loc[
+            (
+                (dataframe[f'btc_{stake}_rsi_1h'] < 35)
+                &
+                (dataframe['eth_btc_rsi_1h'] < 50)
+                &
+                (dataframe['BTC_rsi_fast_upper_1h'] < 40)
+                &
+                (dataframe['btc_rsi_super_fast_1h'] < 30)
+                &
+                (dataframe['rsi_30m'] < 40)
+                &
+                (dataframe['rsi_1h'] < 40)
+                &
+                (dataframe['rsi'] < 30)
+                &
+                (dataframe['rsi_less'] == True)
+                &
+                (dataframe['volume'] > 0)
+            ),
+            ['enter_long', 'enter_tag']] = (1, 'buy_signal_rsi')
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        dataframe.loc[
+            (
+                (dataframe['rsi'] > 70)
+                &
+                (dataframe['rsi_less'] == False)
+                &
+                (dataframe['volume'] > 0)
+            ),
+            ['exit_long', 'exit_tag']] = (1, 'exit_signal_rsi')
+
+        return dataframe