【各ソース】

　2-1 sqlite操作モジュールを作成

・Create部分

import sqlite3

conn = sqlite3.connect('FX.sqlite3')

c = conn.cursor()

c.execute('create table position_details(securities_company TEXT,symbol TEXT,lots INTEGER,type INTEGER,profit REAL,profit_pips REAL,tax REAL,swapu REAL,open_price REAL,close_price REAL,open_time TEXT,close_time TEXT)')

conn.commit()

conn.close()

・Select Insert部分 ※ヒューマンエラーにてCreateとはモジュールを分けました。

import sqlite3

import pandas

# dict_factoryの定義

def dict_factory(cursor, row):

d = {}

for idx, col in enumerate(cursor.description):

d[col[0]] = row[idx]

return d

# クラスの定義

class table:

conn = sqlite3.connect('FX.sqlite3')

conn.row_factory = dict_factory

c = conn.cursor()

# インサートの定義

def insert(self, securities_company, symbol, lots, type, profit, profit_pips, tax, swapu, open_price, close_price, open_time, close_time):

self.c.execute("insert into position_details values "

"(:securities_company,"

":symbol,"

":lots,"

":type,"

":profit,"

":profit_pisp,"

":tax,"

":swapu,"

":open_price,"

":close_price,"

":open_time,"

":close_time)"

,

(securities_company,

symbol,

lots,

type,

profit,

profit_pips,

tax,

swapu,

open_price,

close_price,

open_time,

close_time))

# セレクトの定義

def select(self):

self.c.execute('select * from position_details')

data = self.c.fetchall()

return data

　2-2 csvファイルを読み取り用モジュールを作成

# ヒロセ通商

def datainsert(sql, pandas, math):

for i in range(61):

list = pandas.read_csv(★読み取りたいcsvパスを設定してください)

for j in range(len(list)):

sql.insert("ヒロセ",

list.at[j,"通貨ペア"].replace("/", ""),

(list.at[j,"Lot数"].astype(float) * 1000),

list.at[j,"売買"],

math.floor(list.at[j,"決済損益"].astype(float)),

math.floor(list.at[j,"pip損益"].astype(float)),

math.floor(list.at[j,"手数料"].astype(float)),

math.floor(list.at[j,"スワップ損益"].astype(float)),

list.at[j,"新規約定値"],

list.at[j,"決済約定値"],

list.at[j,"新規約定日時"].replace('/', '-'),

list.at[j,"決済約定日時"].replace('/', '-'))

# XMインサート

def datainsert(pandas, math, sql, pipscalculation):

list = pandas.read_csv(★読み取りたいcsvパスを設定してください)

print(list.info())

for j in range(len(list)):

if list.at[j, "銘柄"] != "":

sql.insert("XM MT5",

(pipscalculation.currency_replace(list.at[j,"銘柄"])),

math.floor((pipscalculation.lotscalculation(list.at[j, "銘柄"], list.at[j, "数量"].astype(float)))),

list.at[j,"タイプ"].replace('buy', '買').replace('sell', '売'),

math.floor(list.at[j,"損益"].astype(float)),

(pipscalculation.pipscalculation(list.at[j, "銘柄"], list.at[j, "タイプ"], list.at[j, "価格"], list.at[j, "価格.1"])),

math.floor(list.at[j,"手数料"].astype(float)),

math.floor(list.at[j,"スワップ"].astype(float)),

list.at[j,"価格"],

list.at[j,"価格.1"],

list.at[j,"時間"].replace('.', '-'),

list.at[j,"時間.1"].replace('.', '-'))

※pipscalculationについては省きます。

　そんな難しいことはしていないです。

　2-3 mainにて実行

import SQLITE.position_details.SQL

import pandas

import math

from csvexeport import csv_exeport, csv_exeport_rate

from csvInsert import titan_insert, xm_insert, hirose_insert, rate_insert

import datetime

from dateFormat import dateformat

from pip import pipscalculation

sql = SQLITE.position_details.SQL.table('FX.sqlite3', 'position_details')

# ヒロセ通商 csvinsert

hirose_insert.datainsert(sql, pandas, math)

# XM　csvinsert

xm_insert.datainsert(pandas, math, sql, pipscalculation)

トレード分析